[OutisLi]

Summary

This PR combines two related ASE improvements: compiling eligible checkpoints at calculator construction, and reducing the neighbor-search and graph-preparation work performed on every evaluation. It also exposes single-pass PT-expt compression during freezing, which ASE selects automatically for supported DPA4C models.

The changes are rebased onto master after #6045, so automatic compression uses the shared model capability interface already present upstream. The ASE stress symmetrization from #5799 is retained.

from deepmd.calculator import DP

calculator = DP(
    model="model.pt",
    freeze=True,
    neighbor_graph_method="auto",
    neighbor_skin=1.0,
)

To reuse the compiled artifact without compiling again:

calculator = DP(model="model-ase_single.pt2")

Compression is also available directly:

dp --pt-expt freeze -c model.pt -o model.pt2 --compress

Checkpoint freezing and compression

• Add DP(..., freeze=True) for PT SeZM/DPA4 checkpoints and graph-eligible PT-expt checkpoints. Eligibility is determined from the loaded model and selected head, rather than from the file extension alone.
• Release the eager evaluator before invoking the existing backend exporter. Each opted-in construction rebuilds and loads a private artifact owned by its evaluator, then atomically publishes a separate copy as <stem>-ase_single.pt2 beside the checkpoint. Concurrent processes retain their selected heads and later serialization keeps reading the private artifact; failed export/loading/publication preserves the previous public copy. The last successful publication wins. The extra private copy is removed when its evaluator is collected. Backend compilation is process-concurrent, not thread-safe; reuse the public copy after concurrent exporters finish. Unsupported models and already-frozen files keep their normal loading behavior.
• Preserve multi-task head selection and normalize single-task PT heads to None, matching the eager loader.
• Add with_comm to the exporters with a backward-compatible default of True. ASE passes False, compiles only the single-process artifact, and records has_comm_artifact=False.
• Reuse the backend kernel policy: CUDA DPA4 defaults to DP_CUDA_INFER=1 and DP_TRITON_INFER=2, honoring explicit environment overrides; DPA4C retains its required CUDA inference level of 2.
• Keep freeze=False as the default. Eligible checkpoints warn about the potentially slower eager path; that path remains useful for debugging and short runs that do not amortize compilation.
• Add --compress / compress=True to PT-expt freezing. The freeze and existing compress commands share the model-compression/export pipeline, including neighbor-distance handling and table defaults. Compression happens before export, without compiling an intermediate uncompressed artifact. Other backends reject freeze --compress during argument parsing. When saved neighbor-distance statistics are missing, the error directs CLI users to freeze without compression and then use dp --pt-expt compress ... -t INPUT.json.
• Fold vacuum references before specializing charge-dependent compression tables. Eligible CUDA targets use the compact canonical input schema; CPU targets use the graph schema.
• Share the DPA4C compression eligibility check between explicit compression and ASE selection. Unsupported precision, type exclusions, compiled profiles, or missing initial charge states produce an explicit error for --compress; automatic ASE compression logs the reason and continues with ordinary freezing. Other model families retain uncompressed export.
• Leave CompositeImplicitAutograd operators to their decompositions instead of registering conflicting independent fake/Meta implementations.

Persistent neighbor searches and graph preparation

Search policy and cache lifetime

• Expose neighbor_graph_method and neighbor_skin at the ASE boundary and forward them through DeepPot, including the reload after automatic freezing. ASE defaults to a 1.0 angstrom skin; direct evaluator callers retain their zero-skin default.
• Prefer Vesin for single-frame CUDA graph inference and Toolkit-Ops for multi-frame CUDA batches. Preserve native CPU graph search, explicit backend choices, and native fallback when optional search dependencies are absent.
• Share the extended-coordinate/edge-vector neighbor strategy factory across PT and PT-expt. SeZM/DPA4 applies inference selection in its geometry stage, preserving native forward and compiled core execution; training retains its own policy.
• Keep a persistent Vesin calculator per frame. Reuse candidate lists extending to rcut + skin, rebuilding after sufficient displacement or changes to the cell/atom count. Every evaluation still refreshes geometry and enforces the exact model cutoff. Returned search data are copied so later evaluations cannot overwrite tensors needed by earlier autograd graphs.
• Cache element-to-model type mappings until atomic numbers, atom order, or type_dict changes. neighbor_skin=0.0 disables candidate reuse.

Compact graph inputs

• Construct compressed DPA1/DPA4C canonical inputs directly from Vesin displacement vectors. Produce destination-major payloads and both CSR views, with uint32 source/order arrays, float32 geometry, and guard storage for zero or one physical edge.
• Avoid the generic graph conversion and its tensor-value reads on the host when direct canonical preparation applies. Keep generic conversion for other neighbor builders and pair-excluded graphs.
• Use PyTorch destination sorting on CUDA instead of Vesin sorting. Preserve differentiable coordinate/cell geometry in the generic graph path, select virtual endpoints once, and reuse NV destination ordering where exclusion semantics allow it.
• Avoid per-edge cell replication and frame-offset work for a single frame; pass a single edge-vector frame through without concatenating its arrays.
• Centralize graph input packing for ordinary and native-spin inference while preserving their generic/canonical schemas, optional fitting inputs, and compressed charge-state handling.

Compatibility and integration

• Restrict CPU horizontal fusion for PyTorch 2.11 inference in the shared compiler compatibility options. Its fused gather/scatter code can reuse an index array before its declaration. This avoids the reproduced tmp40 C++ compilation failure while retaining SIMD and vertical fusion; training, CUDA kernel fusion, and other PyTorch versions keep their existing configuration. No export/parity test is skipped or replaced with a mocked compile.
• Make local spin samples contiguous before PT edge-vector export, so traced frame strides do not depend on ghost storage and work for batched input. Existing affected native-spin edge-vector artifacts must be regenerated to obtain this layout correction.
• Restrict the Vesin CUDA empty-cache workaround to the specific known pointer error with skin enabled. An uncached retry is accepted only when it returns an empty result; unrelated failures and nonempty retries still raise the original exception.
• Load PTE archives within a CPU device context at both inference and serialization boundaries. This avoids accidental default-device placement of the storage wrapper in PyTorch 2.13 while retaining tensor devices recorded in the archive.
• Require both vesin and vesin-torch >= 0.6 on supported PyTorch targets. Older PyTorch targets and macOS x86_64 retain native neighbor construction without requiring these bindings.
• Put the shared tiny SeZM test configurations in a backend-neutral test module. Collecting the PT-expt ASE tests no longer initializes the PT test package, repeats its global interop-thread setup, or inherits its sentinel default device.
• Scope the synthetic CUDA device query to entering the real export-policy context in the policy test. Lazy imports during model/schema construction retain the actual query, so subsequent CPU exports cannot inherit a mocked CUDA target. The real policy and environment-restoration checks remain covered.
• Document the calculator options, supported input schemas, cache invalidation, compression fallback, and artifact reuse in the ASE guide.

Validation

Cell-cache review validation (2026-10-21)

• Rebuild only the affected frame's Vesin calculator when its cell values or periodicity change. Store an independent detached cell snapshot so in-place cell updates are detected; fixed-cell searches keep reusing their calculator. Skin-zero searches do not compare or snapshot cells.
• The one-atom, multiple-periodic-image contraction regression fails before the fix and passes afterward for both image shifts and displacement vectors. It also checks fixed-cell reuse, in-place contraction, frame isolation, and periodic/nonperiodic transitions.
• Correct the default neighbor-builder expectation and test the actual strategy factory across CPU/CUDA and Vesin/NV availability combinations.
• CPU validation: 87 passed, 26 skipped, 38 subtests passed, covering Vesin graph/search behavior, checkpoint inference, nlist integration/builders, and cached ASE energy/force/stress parity. Independent review found no actionable issues; scoped pre-commit, repository Ruff, and whitespace checks passed.
• Comparing cached periodic CUDA cells synchronizes the rebuild decision. This is a correctness tradeoff; its overhead and GPU execution were not measured in this review fix.

Review-fix validation (2026-10-19)

• PyTorch 2.13 CPU: the full ASE calculator, freeze/compression, graph-builder dispatch, graph-archive metadata, and CLI argument-parser files passed 152 tests, with 15 existing conditional skips. The compiler-compatibility file passed 9 tests separately.
• PyTorch 2.11.0+cpu with AVX2 code generation: the complete freeze/compression and compiler-compatibility files passed 23 tests, including actual float64 AOTI export and energy/force/stress comparisons. The undeclared-index-temporary failure was reproduced before the production compiler-option fix.
• All five artifact-ownership/concurrency/failure checks failed before the ownership fix and passed afterward. The negative CLI cases and both stale regression expectations were also reproduced before their corrections. The concurrency test controls file I/O with exporter doubles; actual compilation is covered by the sequential export tests.
• Final independent review found no actionable issues. Scoped pre-commit hooks, repository Ruff, and whitespace checks passed.
• These review fixes were validated on CPU; CUDA and performance/MD matrices were not rerun. The earlier CUDA and benchmark results below remain historical validation, not new measurements.

Rebased-branch validation

Validation uses Python 3.13, PyTorch 2.13.0+cu130, and Vesin / vesin-torch 0.6.1. CUDA runs use one RTX PRO 6000 Blackwell Server Edition GPU. Tests generate their own small models/artifacts; the benchmark matrices below were not rerun.

• Full CPU ASE/freezing/compression/graph-utility/export-policy suite: 109 passed, 32 skipped, including real single-artifact exports, native-spin edge-vector reloads, DPA4C compressed export, and the float64 uncompressed fallback.
• CPU PT neighbor-backend and PT-expt checkpoint inference suites: 48 passed, 7 skipped, 34 subtests passed.
• CPU PTE default-device, frame/atomic-parameter, and neighbor-backend checks: 13 passed, 2 skipped, 2 subtests passed.
• The upstream asymmetric-virial ASE stress regression: 1 passed.
• CUDA canonical/Vesin graph checks, cached-neighbor/type parity, and real automatic freeze/reload comparisons: 32 passed. This includes actual compilation and energy/force/stress comparisons for PT SeZM/DPA4 and PT-expt DPA4, DPA4C, DPA1 and DPA2.
• The policy-mock/CPU-compression sequence reproduced the test-isolation failure before the scoped-mock correction and passes all 13 checks afterward. This correction changes only the test harness, not production device selection or numerical tolerances.
• Repository Ruff, scoped pre-commit hooks, and whitespace checks pass. Independent review found no remaining actionable issues and additionally checked bridged/native-spin DPA4C compression/serialization and preservation of the analytical component. That additional review simulated final artifact writing; actual AOTI compilation is covered by the tests above.

The full repository test suite and the historical performance/MD matrices were not rerun for this rebase. CPU-only runs skip CUDA-conditional cases; missing-dependency branches also skip when the optional dependency is installed.

Original development validation

The original two commit messages record the following development runs. These groups cover different stages and may overlap; they are not an aggregate test count or a claim that every run was repeated for this rebase.

• Freezing: 44 checkpoint/head/kernel/export-policy checks; 43 CPU export/compatibility checks; two existing ASE calculator tests; and five real CUDA export/ASE round trips across PT SeZM/DPA4 and PT-expt DPA4, DPA4C, DPA1, and DPA2. Checks include energy, forces, stress, reload, source-checkpoint integrity, and exactly one compilation per ASE export.
• Compression: 57 focused CLI/shared-pipeline/fallback/error/fake-op checks; actual float64 fallback export; compressed CPU and CUDA ASE exports with compression operators in the exported graph; and trained dpa4c-air-v20260911 checkpoint comparisons.
• Graph/input preparation: 53 CPU/CUDA graph-utility checks, 28 CPU inference checks, 13 CPU edge/neighbor checks, and three CUDA compressed charge-state route checks. Coverage includes dual CSR, zero/one edge, empty systems, virtual atoms, multiple frames, periodic geometry, cache updates, result lifetime, gradients, and empty/nonempty transitions.
• All 22 frame/atomic-parameter and spin-parameter checks passed after the archive-loading correction. Default-device regressions covered CPU/CUDA execution under meta and invalid-CUDA defaults; the corresponding cases failed before the correction.
• 120 real exported-model comparisons covered ordinary/compressed Air, DPA1 canonical and DPA4C native-spin canonical inputs, multiple frames, isolated/padded atoms, and periodic/nonperiodic systems.
• All 24 paired MD initial-state comparisons passed, with maximum differences of 1.074e-5 eV/angstrom in force and 1.202e-7 eV/atom in energy. The larger scaling matrices below additionally passed all 54 Mini and 120 Air initial energy/force/stress comparisons, with model/artifact/runtime-library hashes verified.

Original performance measurements

The following results are reproduced from the original commit messages. They were measured during development, not rerun against the rebased branch. Each experiment has its own baseline; speedups from separate tables should not be multiplied. Timings exclude initialization/export/warmup unless explicitly stated and describe the measured workloads, not a universal performance guarantee.

Eager versus frozen ASE evaluation

Preset dpa4c-air-v20260911, float32 with vacuum_ref enabled, PyTorch 2.13.0+cu130, one NVIDIA RTX PRO 6000 Blackwell Server Edition GPU. Three OMat24 validation structures and periodic supercells were evaluated with 20 warmup steps and five timed blocks of 40 steps, alternating execution order. Coordinates changed every step to invalidate the ASE cache; GPU synchronization bracketed each block. The median timings include neighbor construction and transfers. Inference environment overrides were unset.

Atoms	Eager (ms/step)	Frozen (ms/step)	Speedup
16	15.85	4.54	3.49x
64	16.23	4.63	3.51x
128	16.35	4.68	3.50x
1,024	17.20	5.91	2.91x
8,192	24.17	11.86	2.04x

Eager initialization took 4.38 s, automatic freezing/loading 50.91 s, and direct reload of the compiled artifact 0.26 s in that benchmark process. Maximum frozen/eager differences were 4.47e-7 eV/atom in energy, 1.67e-5 eV/angstrom in force components, and 2.91e-8 eV/angstrom^3 in stress. The source checkpoint was unchanged.

Ordinary versus compressed frozen ASE evaluation

The same preset, precision, vacuum-reference setting, PyTorch version, OMat24 structures and supercells were used. Coordinates changed each step and execution order alternated. End-to-end force-evaluation timings include neighbor construction/transfers; export and warmup are excluded.

CUDA: one RTX PRO 6000 Blackwell Server Edition GPU, 20 warmup steps, five blocks of 40 steps, synchronized around each timed block.

Atoms	Frozen (ms/step)	Compressed (ms/step)	Speedup
16	4.67	3.64	1.28x
64	4.73	3.70	1.28x
128	4.82	3.75	1.28x
1,024	6.03	4.99	1.21x
8,192	12.00	6.19	1.94x

CPU: Intel Xeon Platinum 8558P, OMP_NUM_THREADS=1 and one PyTorch compute thread, 10 warmup steps, three blocks of 20 steps.

Atoms	Frozen (ms/step)	Compressed (ms/step)	Speedup
16	41.66	1.55	26.91x
128	64.57	6.67	9.68x
1,024	242.45	44.86	5.40x

Maximum compressed/eager differences:

Device	Energy (eV/atom)	Force components (eV/angstrom)	Stress (eV/angstrom^3)
CUDA	5.66e-7	1.78e-5	7.25e-8
CPU	6.29e-7	1.43e-5	6.54e-8

Compressed export/loading took 26.60 s on CUDA and 13.60 s on CPU in those processes. Compression increases artifact size: the measured CUDA artifact grew from about 15 MiB to 145 MiB because it contains frozen tables. Source checkpoints and earlier exported artifacts were preserved.

Isolated graph/input-preparation effect

RTX PRO 6000 Blackwell Server Edition and Xeon Platinum 8558P, PyTorch 2.13.0+cu130, one CPU thread and identical frozen artifacts. Complete ASE NVE steps used a 1 fs timestep, 300 K initial velocities, 20 warmup steps and 100 timed steps, with three alternating before/after repetitions.

The reference in this experiment already uses cached Vesin searches. It isolates the graph/input-preparation changes within that shared design, rather than comparing the complete PR with unmodified ASE.

Preset / artifact	Atoms	Before (ms/step)	After (ms/step)
Mini, ordinary	512	9.16	9.22
Mini, ordinary	8,000	92.56	92.50
Air, ordinary	512	6.67	5.81
Air, ordinary	8,000	36.50	32.60
Air, compressed	512	4.22	2.61
Air, compressed	8,000	10.04	4.69

Compressed Air improved by 1.62x / 2.14x at 512 / 8,000 atoms. Mini showed no stable end-to-end improvement. Shared-host load varied: compressed Air at 8,000 atoms ranged from 8.33–10.11 ms before and 3.58–5.06 ms after.

Complete ASE optimization and deployment scaling

Here, "before" means the parent of the neighbor/graph optimization commit and "after" includes that complete optimization. This is distinct from the isolated graph-preparation experiment above.

One RTX PRO 6000 Blackwell Server Edition GPU per run, Xeon Platinum 8558P CPUs, PyTorch 2.13.0+cu130, TF32 disabled, and one MPI rank for LAMMPS. All variants use diamond carbon with identical coordinates and 300 K initial velocities, NVE at 1 fs, 20 warmup steps and 100 timed steps, repeated three times. Values are median complete-MD throughput in atoms/s, rounded to integers; export, compression, loading and warmup are excluded.

Checkpoint rows use PT for DPA4 and PT-expt for DPA4C. Freeze rows use uncompressed PT-expt .pt2 artifacts; freeze + compress rows use compressed artifacts. Each frozen artifact is shared across its frontend comparisons. Ordinary LAMMPS uses the allocated CPU thread count. Kokkos uses device MD/neighbor-list execution with one host thread, the same CPU allocation and DeePMD intra-op limit, and the shared PT-expt model.

DPA4 preset Mini: eight physical CPU cores per run. Independent runs execute concurrently on separate GPUs and disjoint GPU-local CPU cores; all variants in a repetition use the same GPU.

Variant	512 atoms	3,136 atoms	9,680 atoms
ASE .pt before	13,188	17,505	18,214
ASE .pt after	13,480	18,500	18,479
ASE freeze before	56,141	77,661	80,307
ASE freeze after	65,248	85,011	87,357
LAMMPS freeze	76,718	87,101	87,988
LAMMPS Kokkos freeze	75,165	87,353	88,945

DPA4C preset Air: 24 physical CPU cores local to the GPU per run; all runs execute serially.

Variant	512 atoms	3,136 atoms	9,680 atoms	49,248 atoms
ASE .pt before	29,867	93,962	116,377	118,342
ASE .pt after	32,049	110,497	122,495	120,628
ASE freeze before	83,740	175,846	230,227	239,501
ASE freeze after	117,170	220,846	252,397	249,470
ASE freeze + compress before	113,548	456,463	1,052,187	1,510,584
ASE freeze + compress after	253,795	1,268,573	2,199,040	2,698,941
LAMMPS freeze	156,523	198,797	203,997	180,545
LAMMPS Kokkos freeze	169,509	247,170	269,813	269,648
LAMMPS freeze + compress	310,375	794,813	927,621	753,016
LAMMPS Kokkos freeze + compress	585,428	2,836,777	4,239,738	6,439,693

All 54 Mini and 120 Air initial energy/force/stress comparisons passed. Across these scaling matrices, the maximum initial force difference was 1.848e-5 eV/angstrom and the maximum energy difference was 5.886e-7 eV/atom.

Summary by CodeRabbit

• New Features

  • ASE can freeze eligible PyTorch models for inference, with automatic compression for supported DPA4C models.
  • Configure neighbor-search methods and skin distance in ASE and inference workflows; automatic selection adapts to device and frame count.
  • Freeze models with optional compression and choose whether to include communication artifacts.
  • Compatible PyTorch installations can use Vesin-based neighbor search.

• Bug Fixes

  • Improved graph inference, device-independent model loading, and single-frame handling.
  • Unsupported automatic compression falls back with a warning; explicit compression reports unmet requirements.
  • Invalid neighbor skin values are rejected.

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Walkthrough

This change adds configurable and cached neighbor searches, updates graph inference preparation, and adds ASE checkpoint freezing and compressed freezing for PyTorch Exportable models. It also updates serialization, dependency selection, runtime compatibility, and tests.

Changes: Inference and export behavior

Layer / File(s)	Summary
Persistent neighbor search and graph construction deepmd/pt_expt/utils/*, deepmd/dpmodel/utils/*, source/tests/pt_expt/utils/*	Adds reusable Vesin searches, adaptive backend selection, canonical graph construction, and frame-aware graph handling.
Backend routing and graph evaluation deepmd/pt/infer/*, deepmd/pt/model/*, deepmd/pt_expt/infer/*, source/tests/pt_expt/infer/*, source/tests/pt/model/*	Adds neighbor backend and skin options, shared graph input preparation, and native-spin routing.
Checkpoint freezing and compression deepmd/pt/entrypoints/*, deepmd/pt_expt/entrypoints/*, deepmd/pt_expt/utils/serialization.py, deepmd/pt_expt/kernels/*, deepmd/main.py, source/tests/pt_expt/test_freeze_compress.py, source/tests/pt_expt/utils/test_serialization_kernel_levels.py	Adds communication-artifact control, compressed freezing, compression eligibility diagnostics, and CPU-staged archive loading.
ASE calculator integration and validation deepmd/calculator.py, doc/third-party/ase.md, doc/model/dpa4c.md, source/tests/pt_expt/test_calculator.py, source/tests/common/sezm_config.py	Adds calculator neighbor and freeze options, checkpoint export, atom-type caching, and related documentation and tests.
Dependency and runtime compatibility backend/find_pytorch.py, deepmd/pt/utils/compile_compat.py, deepmd/pt_expt/utils/tabulate_ops.py, source/tests/pt/test_compile_compat.py, source/tests/pt_expt/utils/test_tabulate_ops_lazy.py	Makes Vesin requirements conditional and adjusts inference compilation and fake operator registration for specific runtime conditions.

Priority: Normal

Estimated code review effort: 4 (Complex) | ~60 minutes

Change: Feature

Possibly related issues

• [BUG] DPA4C compression fails on RTX 4090 with CUDA: "pair_film matches 0 constants" #6062: The issue describes a CUDA DPA4C .pt2 compression failure, and this change updates related compression and serialization paths, but the available evidence does not show that it fixes that failure.

Merge Risk: Low · previous review ae7a9

Some edge-vector evaluations can produce incorrect results, while automatic evaluation of those artifacts can fail on native-only installations. These bounded risks should be addressed or explicitly accepted before merging.

Pre-merge checks: 4 · 1

Failed checks: 1 warning

Check name	Status	Explanation	Resolution
Docstring Coverage		Docstring coverage is 51.33% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 150 functions across 40 files. (1 skipped…	Write docstrings for the functions missing them to satisfy the coverage threshold.

Passed checks: 4 passed

Check name	Status	Explanation
Description Check		Check skipped - CodeRabbit’s high-level summary is enabled.
Title check		The title clearly and concisely identifies the two main changes: ASE checkpoint freezing and reusable neighbor searches.
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.

Full details: Docstring Coverage

Explanation

Docstring coverage is 51.33% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 150 functions across 40 files. (1 skipped: 1 unsupported.)

Resolution

Write docstrings for the functions missing them to satisfy the coverage threshold.

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Actionable comments posted: 1

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• 🪄 Fix comments: Fix CodeRabbit comments on this PR

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Inline comments:
Review comments at @deepmd/calculator.py:
- Around line 230-243: Update the `freezer` call in the `freeze` branch to write
to a unique temporary artifact in the same directory, then atomically replace
`output` with the completed artifact before loading it with `DeepPot`. Ensure
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📥 Commits

Reviewing files that changed from the base of the PR and between 668b60c and 946fbae.

📒 Files selected for processing: 36

• backend/find_pytorch.py
• deepmd/calculator.py
• deepmd/dpmodel/utils/neighbor_graph/from_ijs.py
• deepmd/dpmodel/utils/neighbor_list.py
• deepmd/main.py
• deepmd/pt/entrypoints/freeze_pt2.py
• deepmd/pt/infer/deep_eval.py
• deepmd/pt/model/model/sezm_model.py
• deepmd/pt_expt/entrypoints/compress.py
• deepmd/pt_expt/entrypoints/main.py
• deepmd/pt_expt/infer/deep_eval.py
• deepmd/pt_expt/kernels/dpa4c/graph_compress.py
• deepmd/pt_expt/utils/auto_neighbor_list.py
• deepmd/pt_expt/utils/canonical_graph.py
• deepmd/pt_expt/utils/edge_schema.py
• deepmd/pt_expt/utils/graph_builder.py
• deepmd/pt_expt/utils/nv_graph_builder.py
• deepmd/pt_expt/utils/serialization.py
• deepmd/pt_expt/utils/tabulate_ops.py
• deepmd/pt_expt/utils/vesin_graph_builder.py
• deepmd/pt_expt/utils/vesin_neighbor_list.py
• deepmd/pt_expt/utils/vesin_search.py
• doc/model/dpa4c.md
• doc/third-party/ase.md
• source/tests/common/sezm_config.py
• source/tests/pt/model/test_nlist_backend.py
• source/tests/pt/model/test_sezm_export.py
• source/tests/pt/model/test_sezm_vacuum_freeze.py
• source/tests/pt_expt/infer/test_deep_eval.py
• source/tests/pt_expt/infer/test_deep_eval_pt_checkpoint.py
• source/tests/pt_expt/test_calculator.py
• source/tests/pt_expt/test_freeze_compress.py
• source/tests/pt_expt/utils/test_canonical_graph.py
• source/tests/pt_expt/utils/test_serialization_kernel_levels.py
• source/tests/pt_expt/utils/test_tabulate_ops_lazy.py
• source/tests/pt_expt/utils/test_vesin_graph_builder.py

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[njzjz-bot]

CI-backed follow-through at exact head 946fbae02d77d04e547846850c5a76216c39c272.

The now-completed Python workflow fails in three shards. Two failures are unchanged regression expectations that were not updated with intentional behavior/message changes; the third is the new real float64 ASE fallback-export test encountering CPU Inductor code generation on the repository-pinned PyTorch 2.11.0 stack. The inline findings are limited to those integration blockers. The compiler diagnostic is not evidence of incorrect energies/forces or proof that this PR introduced the underlying compiler defect.

I checked the exact source/test changes, actual CI job logs, and existing reviews before posting. The two stale expectations were also reproduced with exact-source AST/availability doubles; I did not run local PyTorch/AOTI or GPU tests. The already-reported shared-output artifact race is not duplicated here.

Agent: dot

[wanghan-iapcm]

The fan-out review found no >=80-confidence code issues — the deep_eval input-packing refactor was traced slot-by-slot against the old ABI and is behavior-preserving, and the 7 sub-threshold findings are usability notes (two inline below, non-blocking). What blocks this PR is CI: three test failures, two of them deterministic regressions introduced here, all inline.

Current state: 39 pass / 5 fail / 5 skipping (skips are the usual release/GPU gates, same profile as recently merged PRs). The 5 fails are the three Test Python shards below, the all-green aggregator, and RTD — the RTD build shows as CANCELLED on their API, which is infrastructure; it needs a rebuild but is not obviously a content problem.

CI-1 and CI-2 are one-line stale-test updates: this PR changed production behavior/messages without updating the pre-existing tests that pin them. CI-3's new test performs a real AOTI compile with no environment guard and cannot pass on the CI image (no OpenMP). Please address all three; the two non-blocking usability notes are optional.

[njzjz-bot]

Corrective-delta review at exact head 5885aab7589d0f569b00c453be3d212c0b2a0fc5, following 946fbae02d77d04e547846850c5a76216c39c272.

The two stale regression expectations from my previous review are addressed: the single-frame CUDA auto-selection row now expects Vesin while the multi-frame NV case remains, and the nlist-artifact rejection test matches the actionable use nlist_backend message while retaining its default-loading check. Exact-source dispatch checks passed all nine rows.

The compiler mitigation targets the demonstrated failure instead of skipping it: PyTorch 2.11 inference receives cpp.max_horizontal_fusion_size=1, with SIMD, training policy and other releases unchanged. The real float64 ASE freeze/energy/force/stress regression remains intact. Across 96 exact-source old/new option comparisons, the only policy change was this intended inference/version gate. The author's reported 23 native PyTorch 2.11 CPU tests remain attributed to that report; my local checks did not run Torch or AOTInductor.

I also checked the artifact correction against loading and later serialization: each evaluator owns a private artifact, the public copy has a separate inode and is published atomically, and cleanup follows backend lifetime. Exact-constructor host doubles passed sequential ownership, 20 synchronized concurrent head pairs, public-overwrite isolation, retained evaluator/wrapper and cyclic-GC lifetime, plus export/load/copy/publication failure cleanup. These are filesystem/weakref checks, not concurrent compiler tests; the documentation correctly requires separate processes for concurrent export. The new unsupported-backend compression error and actionable statistics guidance are present; 55 stdlib argparse cases with backend-registry doubles passed.

No new defect was found in this bounded corrective delta. Current-head Python CI is still running, so the former native CPU export blocker is not yet independently cleared by hosted results. This scoped follow-up does not substitute for the requested human re-review or overall approval.

Agent: dot

[codecov]

Codecov Report

❌ Patch coverage is 93.78378% with 23 lines in your changes missing coverage. Please review.
✅ Project coverage is 78.08%. Comparing base (a78bdfe) to head (f3268b7).
⚠️ Report is 6 commits behind head on master.

Files with missing lines	Patch %	Lines
deepmd/pt_expt/utils/vesin_search.py	81.08%	7 Missing ⚠️
deepmd/pt_expt/infer/deep_eval.py	92.06%	5 Missing ⚠️
deepmd/pt_expt/utils/auto_neighbor_list.py	92.45%	4 Missing ⚠️
deepmd/pt_expt/utils/vesin_graph_builder.py	92.15%	4 Missing ⚠️
deepmd/pt_expt/entrypoints/compress.py	88.88%	1 Missing ⚠️
deepmd/pt_expt/utils/canonical_graph.py	94.73%	1 Missing ⚠️
deepmd/pt_expt/utils/nv_graph_builder.py	0.00%	1 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##           master    #6085      +/-   ##
==========================================
- Coverage   78.08%   78.08%   -0.01%     
==========================================
  Files        1171     1178       +7     
  Lines      141663   142750    +1087     
  Branches     5073     5072       -1     
==========================================
+ Hits       110621   111467     +846     
- Misses      29104    29347     +243     
+ Partials     1938     1936       -2     

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[iProzd]

With Vesin selected and ASE's default neighbor_skin=1.0, cell contraction can omit valid periodic neighbors. On CPU with Vesin 0.6.1, the one-atom rcut=6 example below returns four neighbors after contraction, while merge-base and uncached searches return six. Please rebuild candidates on cell changes and add this multi-image regression.

[njzjz-bot]

Agent: dot

Reviewed only the 5885aab → 7d57390 review-fix delta and necessary cache/factory context. The reported periodic-cell contraction defect is addressed in source: each frame now owns an independent detached cell snapshot; changed cell values or periodicity reconstruct that frame's calculator, while identical cells keep reuse. Cloning the snapshot detects in-place edits. The default-builder expectation now includes CUDA+NV and the strategy regression exercises the real factory with availability doubles.

The added real-Vesin regression checks the specific 2.35→1.95 cell contraction at cutoff6 (four→six images), both shifts and displacement vectors, fixed-cell reuse, frame independence and periodic/nonperiodic transitions. I independently ran eight bounded exact-source invalidation controls using tensor/neighbor doubles, covering those state transitions, cutoff changes and the skin-zero path; all passed. This local environment has neither PyTorch nor vesin-torch, so those controls are not a reproduction of geometric neighbor counts. The author's 87-test/38-subtest CPU result is existing qualification evidence, not a suite I reran.

No new actionable defect found in the delta. CUDA cell comparison synchronizes the rebuild decision; GPU execution and overhead of this change remain unmeasured, as disclosed. CI is still running. Review only; no code changes or merge actions.

[iProzd]

With skin configured at construction, the original contraction regression now returns all six neighbors on CPU and CUDA at 7d57390. Fixed-cell reuse and per-frame invalidation are preserved, and the optional-backend expectation is corrected. Changing cells deliberately rebuilds candidates; full ASE/MD performance has not been remeasured.

[njzjz-bot]

Agent: dot

Approve at unchanged head 7d57390, following my existing corrective-delta reviews (5596720429 and 5570005968). The previously reported stale expectations, CPU export/compiler policy and artifact ownership issues are addressed, and the periodic-cell contraction fix is source-LGTM. Current-head Python and C++ workflows now pass. No new commits or actionable review findings have appeared since the reviewed fix.

I am reusing the qualification already recorded rather than duplicating the same-head review: my bounded exact-source controls, the author's 87-test/38-subtest CPU result, and iProzd's subsequent actual CPU/CUDA contraction regression (5598883821) recovering all six neighbors. The latter also verifies fixed-cell reuse and per-frame invalidation. I did not independently rerun native CUDA or the full ASE/MD performance matrix; the synchronization overhead on changing cells remains unmeasured. This approval is not a new performance claim. Review only; no merge actions.

[njzjz-bot]

Agent: dot

Reference correction to my approval above: the earlier corrective-delta review is 5569997776, not the mistyped 5570005968. The approval and its validation scope are unchanged.

[wanghan-iapcm]

The ASE freezing and neighbor-search changes are useful, but I am requesting changes for two automatic-freeze integration issues: native SeZM neighbor truncation and reloading older compiled checkpoints without key normalization. The two additional regression-test comments are non-blocking suggestions and should not hold up merging once the blocking issues are addressed.

Reviewed commit: 7d57390482706e949dbbe4700e080fce5c2e4c47. The findings are based on source inspection; no local runtime reproduction was performed.

[njzjz-bot]

Agent: dot

Correction to my earlier approval (5601787155): I independently checked the newly raised automatic-freeze integration findings in review 5603257071, and am changing my assessment to changes requested at the same exact head 7d57390. These are distinct from the already-fixed periodic-cell contraction issue, whose qualification remains valid.

1. Confirmed the native SeZM edge-set mismatch. The eager self-built SeZM path requests all-cutoff edges from its selected builder. With freeze=True and nlist_backend='native', the reloaded edge_vec artifact has no accelerated builder and routes through dpmodel.utils.nlist.build_neighbor_list with fixed sum(sel), then converts that truncated list to edges. Executing the unchanged dense-builder function through a thin NumPy Array API adapter for three mutually close atoms, rcut=2 and sel=[1,0] yields 3 directed neighbors versus 6 all-cutoff neighbors. This is a bounded builder-level reproduction, not a native frozen energy/force comparison.

2. Confirmed the compiled-checkpoint key incompatibility. I executed the eager loader's exact normalization block: model.Default.original_model.weight becomes model.Default.weight and compiled_forward_lower constants are dropped. The automatic freezer instead reloads the unnormalized raw state into ModelWrapper with strict load_state_dict (both single- and multi-head branches). The raw keyset therefore lacks the expected plain key and has unexpected wrapper/compiler keys. This validates the source-level strict-key mismatch; I did not run a real compiled checkpoint through PyTorch here.

The existing inline threads 4339579553 and 4339579568 already describe the repairs, so I am not duplicating them. Please address both before relying on my approval. I missed these integration paths in the earlier review. The two additional coverage suggestions remain non-blocking; no new GPU/performance or native end-to-end validation is claimed.

[njzjz-bot]

Agent: dot

Incremental review of 681663b → 71506e0: source LGTM for the one-test repair. The four explicit availability combinations now include NV-only CPU, matching make_neighbor_list's supported CPU/CUDA predicate. Patching the probes in auto_neighbor_list controls the names actually read by dispatch, while DeepPot construction/PTE loading remain real; no neighbor numerical output is mocked or newly claimed. Production dispatch and the broader previously reviewed inference tests are unchanged.

This resolves the existing stale AUTO expectation finding without duplicating it. The author's reported 10 CPU tests/34 subtests are reused as reported evidence; I did not execute Torch/NV/CUDA locally. Current Python/C++/wheel CI is still running. Review-only; no branch or merge changes.

[wanghan-iapcm]

The latest commit addresses the stale test_default_is_auto expectation: the four dependency combinations are now exercised with the correct mock target and expectations. Two non-blocking follow-ups are attached inline (a missing negative test for the nv backend and a docstring that does not match the edge-vector behaviour).

[wanghan-iapcm]

Two findings from the re-review at f3268b7: the type-mapping cache test is tautological, and the float64 eager-vs-export comparison uses a float32-grade tolerance.