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fix(vacuum): bound isolated graph references and preserve export kernels - #6080
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Freezing with vacuum_ref ran fold_vacuum_reference before moving the
model to the CPU tracing device, so vacuum_descriptor could OOM on CUDA.
Moving to CPU first avoids that allocation on the GPU.
fold_vacuum_reference replaces bias_atom_e (and may set vacuum_table)
through numpy assignment. dpmodel_setattr previously always placed those
buffers on env.DEVICE, so a fold after Module.to("cpu") pulled tensors
back to CUDA and broke FakeTensor tracing. Place new buffers on the
module's current device instead.
Compute graph-native isolated references without dense sel padding. Keep legacy descriptor reference semantics behind an explicit capability. Fold on CPU reference descriptors without autograd, restoring deployment kernels and retaining fitting reference tables before fake tracing. Add allocation, conditioning, dispatch, export-order and restoration tests.
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Merge Risk: ⚪ Minimal · up to
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| Check name | Status | Explanation | Resolution |
|---|---|---|---|
| Docstring Coverage |  |
Docstring coverage is 68.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 50 functions across 11 files. | Write docstrings for the functions missing them to satisfy the coverage threshold. |
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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: bounded isolated graph references and preservation of export kernels. Linked Issues check The PR meets the coding requirements in [ #6076].DPAtomicModel.vacuum_descriptor()uses bounded graph inputs for graph-capable DPA4 and DPA4C descriptors, so it does not allocate fromget_nnei(). …Out of Scope Changes check The changes remain within [ #6076]. The graph-vacuum capability and bounded-reference tests address excessive vacuum allocation. The CPU reference-descriptor context, NumPy buffer-device selection, and…Full details: Linked Issues check
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Explanation
The PR meets the coding requirements in [
#6076].DPAtomicModel.vacuum_descriptor()uses bounded graph inputs for graph-capable DPA4 and DPA4C descriptors, so it does not allocate fromget_nnei(). The export path moves the model to CPU before folding and folds undertorch.no_grad()with temporary reference descriptors. The context restores descriptors, bindings, device state, and environment settings on success and failure. Tests cover sentinel allocation guards, conditioning, CPU residency, CUDA-only dispatch, graph-route preservation, and restoration. Real CUDA execution and large-checkpoint memory use remain unverified, as stated in the PR description.
Full details: Out of Scope Changes check
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Explanation
The changes remain within [
#6076]. The graph-vacuum capability and bounded-reference tests address excessive vacuum allocation. The CPU reference-descriptor context, NumPy buffer-device selection, and export tests address CPU folding and device consistency. Legacy graph-disable behavior and reference semantics are covered as compatibility support. No unrelated change is demonstrated.
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Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## master #6080 +/- ##
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- Coverage 77.79% 77.56% -0.24%
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Files 1171 1171
Lines 141562 141612 +50
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iProzd
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Reviewed the exact head and the complete vacuum-reference/export diff. The bounded graph path prevents sentinel-sized allocations, preserves legacy dense semantics, and restores deployment bindings after CPU reference folding. Targeted coverage and required checks are green with CUDA labels skipped; I found no blocking issue.
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Thanks for the fix — the graph-vacuum routing and the CPU-fold ordering both look correct to me, and the regression tests genuinely exercise the previously untested paths. One blocking issue on test tolerances (inline), plus two non-blocking nits.
Note on scope: GPU-side execution is unverified (CUDA CI jobs are skipped), as you already called out in the PR description — fine for this PR, but worth tracking.
Use precision-specific dense/graph comparison tolerances without loosening float32 checks. Cover new buffers inheriting parameter or buffer devices with CPU/meta distinctions, and document export test parametrizations.
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Agent: dot
Incremental review of 1cfa25a → fd0a9cd:
- Only three test files changed. Both float64 comparison sites now use 1e-12; the float32 checks were not loosened.
- The new parameter/buffer cases distinguish module residency (
meta) from the process default (cpu), covering both additional device-selection branches. The requested parametrization explanations are also present. - Local validation: parsed the changed tests and relevant unchanged helper, executed the exact precision-selection assignments, and checked the exact device helper with two namespace doubles. I did not run the added PyTorch tests or numerical descriptor comparisons locally.
No new issue found in this bounded delta. At the CI check, six Python shards and two C++ jobs had passed, with the remaining tests still running; docs/pre-commit passed and actual CUDA jobs were skipped. Wanghan's prior-head changes-requested review remains for their follow-up; this comment does not change that review or resolve its threads.
Preserve the bounded, capability-gated graph vacuum route while retaining upstream analytical bridging and node-gate handling. Remove an unsupported redundant compute_precision keyword from the float32 export test fixture. Validation: 63 focused tests and repository-wide Ruff checks pass with a fresh CPU native extension. Bridged DPA4/DPA4C probes cover 16 reference cases and 48 before/after-fold comparisons at 1e-12 tolerance.
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Actionable comments posted: 1
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Inline comments:
Review comments at @deepmd/dpmodel/atomic_model/dp_atomic_model.py:
- Line 437: Update forward_atomic_graph so it appends vacuum frames only when
the fitting requires a vacuum descriptor and the descriptor supports graph
vacuum; otherwise pass self.vacuum_descriptor() to the fitting. When graph
vacuum is supported, continue passing the descriptor computed from the appended
frames.
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📒 Files selected for processing: 7
deepmd/dpmodel/atomic_model/dp_atomic_model.pydeepmd/dpmodel/descriptor/dpa4.pydeepmd/dpmodel/descriptor/dpa4c.pydeepmd/dpmodel/descriptor/make_base_descriptor.pydeepmd/pt_expt/common.pysource/tests/pt_expt/utils/test_common.pysource/tests/pt_expt/utils/test_serialization_vacuum_ref.py
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Append isolated reference frames only for descriptors that opt into graph vacuum evaluation. DPA1 and DPA2 now use the same differentiable dense reference in graph inference and folding; DPA4/C keep the shared graph call. Cover nonzero normalization, unconditioned and fparam/aparam folding, and parameter-gradient parity. Eight regressions fail numerically before the fix; 80 related tests pass afterward, with two GPU tests skipped.
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All three threads from my earlier review are addressed and verified at the current head: fd0a9cd splits the tolerances per precision (float64 now compared at 1e-12) and adds the _module_buffer_device branch coverage with the meta-device canary; 8ac623c keeps the dense vacuum reference for legacy graph descriptors, exactly as the earlier thread suggested. The added tests fail on the pre-fix code and pass at head. CI is genuinely green (54 pass; the 4 skips are release- and GPU-gated, matching the profile of recently merged PRs). Approving.
Summary
Fixes #6076 and supersedes #6079, retaining its module-local NumPy buffer-device fix and CPU-before-fold export ordering.
This addresses both excessive vacuum-reference allocation and a CUDA-target DPA4 export regression introduced by moving the eager fold to CPU.
Changes
get_nnei(). DPA4C's[999999]graph sentinel must not become almost one million padded neighbors per type.supports_graph_vacuum()capability. DPA4/DPA4C opt in; legacy DPA1/DPA2 reference semantics and explicit graph-disable routing remain unchanged. Preserve type, charge and native-spin conditioning.torch.no_grad(), retaining bias/reference tables on the original fittings, then restore deployment descriptors and their optimized bindings before fake tracing. Runtime graph-disable state and environment settings are restored, including failure paths.Simply moving a CUDA-bound float32 DPA4 model to CPU does not unbind its CUDA-only operators. Conversely, disabling deployment kernels throughout tracing would silently lose acceleration. The scoped reference-descriptor context avoids both problems without reconstructing the whole folded model or losing
vacuum_tablethrough serialization.Regression coverage
Validation
.pt2/.ptecompilation, fparam.pt2, and archive reserialization/refreezing. This run used the existing installed CPU native library with current Python source.git diff --checkpassed.Memory sanity check
An untrained default-size DPA4C descriptor with the bundled 89-element OMat24 type map, float64 and charge/native-spin conditioning, retained its actual
get_nnei() == 999999. Vacuum evaluation produced finite(89, 207)output in approximately 0.01–0.03 s, with about 34 MiB incremental process peak RSS. This is a CPU sanity check, not a benchmark of the reporter's pretrained checkpoint or a directly comparable speedup claim.Not verified
The CPU dispatch regression uses an operator without a CPU implementation and checks fake-trace preservation; it is not presented as a GPU integration test.
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