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cache generated PyTorch/PT2 and JAX SavedModel fixtures used by the C++ test suite
reuse generated fixtures only on an exact cache hit, with a manifest check to reject partial caches
include generated .pth, .pt2, .pte, .expected, and .savedmodel artifacts
scope native PT2/TorchInductor caches by OS/arch, CPU ISA flags, glibc, compiler, build variant, and fixture-source fingerprint
avoid broad restore-key fallbacks so native artifacts are never reused across incompatible runner environments
cache the TorchInductor directory with the same native/source scoping
Self-hosted runners
The regular C++ jobs in deepmodeling use the cpu self-hosted runner label. This is compatible with the cache design: heterogeneous runners are separated by the native-environment fingerprint, while a stable self-hosted machine should get better cache reuse. The compiler fingerprint is computed after the workflow's GCC installation step.
Validation
git diff --check passes
cold-cache C++ generation/build/CTest completed successfully in the fork (4/4 CTest targets passed)
LSAN C++ job also completed successfully on the current branch
one fork-hosted regular LAMMPS pytest execution was externally terminated with exit 143; no DeepMD/LAMMPS assertion or exception was reported, and the existing upstream retry_on_sigterm.sh specifically handles this hosted-runner transient
Current cache fingerprint follow-up
Commit fe3b596 installs the Python environment before calculating/restoring the native fixture cache. The key includes the Python implementation/version and sorted installed distribution names/versions, excluding only the editable DeePMD distribution version whose relevant source is already hashed. Native CMake/config/library inputs and the DPA4 fixture helper also invalidate the source fingerprint.
Focused checks verify dependency-version invalidation, stable ordering/repeated execution, and no invalidation from an editable DeePMD version-only change. The exact-shell harness also verifies cold/warm cache control flow, fixture manifests and generator failure propagation. These checks validate cache orchestration; they do not substitute for completed native generation and CTest jobs.
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Summary by CodeRabbit
Tests
Improved reliability of PyTorch-enabled test runs by checking cached fixtures before reuse and reporting errors when required fixtures are missing.
Added an option to reuse generated fixtures when a complete manifest is available.
Kept CUDA-specific fixture generation limited to CUDA runs.
Chores
Improved cache tracking for fixture-generation changes and kept fixture lists consistently ordered.
Removed stale generated files when fixtures are regenerated.
Cache all generated PyTorch fixture formats, including .pth files required when generator execution is skipped on a cache hit. Scope PT2 and TorchInductor native-code caches to the runner CPU/toolchain fingerprint and remove unsafe prefix fallback reuse.
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Avoid invalidating the expensive generated PT/JAX C++ fixtures when unrelated source/tests/infer or backend code changes. Fingerprint only the generator scripts and committed inputs plus the Python/backend/custom-op code that can affect those generated artifacts.
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Model: GPT-5.6 Sol
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Walkthrough
The pull request adds manifest-based reuse for generated PyTorch fixtures. The workflow fingerprints fixture-generation inputs, configures fixture and TorchInductor caches, validates cached directories, and passes cache state to the test script. The fixture-generation script validates, generates, and records fixture files.
The script validates reusable fixtures, removes stale .pt2, .pte, .expected, and manifest files on cold runs, waits for generation processes, and writes a sorted manifest. It applies leak-sanitizer settings during generation when the specified conditions are met.
The workflow fingerprints runner details and selected fixture-generation inputs, configures fixture and TorchInductor caches, validates required SavedModel directories, passes cache state to the test script, and updates the manifest after cold runs. The test checks that changing the shared fixture helper changes the source fingerprint.
sequenceDiagram
participant Workflow
participant Cache
participant FixtureScript
participant Generators
Workflow->>Cache: Restore fixture and TorchInductor caches
Cache->>Workflow: Provide cached files and manifest
Workflow->>FixtureScript: Pass cache-hit state and run tests
FixtureScript->>FixtureScript: Validate manifest and listed files
FixtureScript->>Generators: Generate fixtures on a cold run
Generators->>FixtureScript: Return generated fixture files
FixtureScript->>FixtureScript: Write sorted fixture manifest
Workflow->>Cache: Save generated fixtures and updated manifest
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Possibly related PRs
deepmodeling/deepmd-kit#6116: Adds long-range model generation to the fixture script, changing the generated fixture set and manifest.
deepmodeling/deepmd-kit#6110: Adds fixture generators to the script, changing the artifacts recorded in the manifest.
deepmodeling/deepmd-kit#6085: Changes PyTorch freezing and serialization, which can affect cached .pt2 and .pte artifacts.
Suggested reviewers:njzjz
Merge Risk: Minimal · previous review 54fc7
The fixture-cache integration has no identified merge-blocking defect at this revision. The script remains executable, and fixture reuse includes the corrected fingerprint inputs and sidecar validation.
Pre-merge checks: 5
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Explanation
Title check
The title "ci: cache generated C++ model fixtures" matches the main change. The raw summary shows a workflow that caches generated model fixtures keyed by runner fingerprints, a script change that reu…
Docstring Coverage
No eligible functions found in the changed files. Skipping docstring coverage check. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documentat…
Linked Issues check
Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check
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Description Check
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Full details: Title check
Explanation
The title "ci: cache generated C++ model fixtures" matches the main change. The raw summary shows a workflow that caches generated model fixtures keyed by runner fingerprints, a script change that reuses cached PT fixtures, and a test for the fingerprint inputs. The PR objectives describe the same goal of caching generated PyTorch/PT2 and JAX SavedModel fixtures for the C++ test suite. The title is short, specific, and uses a conventional commit prefix. It does not mention the fingerprint scoping or the test, but those are supporting details.
Full details: Docstring Coverage
Explanation
No eligible functions found in the changed files. Skipping docstring coverage check. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documentation-only edits and deletions within surviving functions are included; unchanged diff context and fully deleted functions are excluded. Analyzed 0 functions across 2 files. (1 skipped: 1 unsupported.)
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Actionable comments posted: 1
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Inline comments:
In @.github/workflows/test_cc.yml:
- Line 76: Update the source_fingerprint computation in the workflow to include
source/install/test_cc_local.sh alongside the existing tracked inputs, ensuring
changes to the fixture orchestrator invalidate the cache and rerun fixture
generation.
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.github/workflows/test_cc.yml
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Reviewed the full two-file change set and the current C++ workflow behavior. The cache design is generally conservative: fixture/TorchInductor restores use exact keys, native PT2 reuse is scoped by ISA/libc/compiler/build variant plus source content, the restored file manifest is checked before generation is skipped, and the generated SavedModel directories that are not represented as manifest files receive explicit existence checks. I did not find a current functional blocker in the cold-cache path.
Two things keep this at COMMENT / needs validation rather than a final approval disposition. First, the exact-head Test C++ run is still in progress (Test Python, CodeQL, and package/PyPI are also still running). More importantly for this PR specifically, pt-cxx-fixtures-v4 is a new cache namespace, so the current exact-head run is necessarily exercising the cold-cache path; the newly introduced DP_REUSE_GENERATED_PT_MODELS=1 cache-hit branch has not yet been exercised by this PR's CI. A warm rerun after the cache is saved is the relevant end-to-end validation for the behavior being added.
One concrete cache-invalidation hardening point: source_fingerprint does not include source/install/test_cc_local.sh, even though that script determines which gen_*.py producers run and under what conditions. A future change that only alters this orchestration can therefore restore an old exact source key and skip the changed generation flow. Including source/install/test_cc_local.sh (and, if desired, other generation-orchestration inputs) in the fingerprint would close that gap. I am treating this as a hardening recommendation rather than a blocker for the current cold-cache head because the v4 namespace is new here.
This PR is authored by njzjz-bot, which is also the executing GitHub account, so GitHub will not accept a self-APPROVE/REQUEST_CHANGES from this account. After relevant CI plus a warm-cache run pass, the final approval must come from an independent reviewer/account.
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Re-reviewed the complete two-file change at the new head. The cache-invalidation gap from the previous round is fixed: source/install/test_cc_local.sh is now included in source_fingerprint, so changes to the fixture-generation orchestrator invalidate both the generated-fixture and TorchInductor cache keys. I also checked the new manifest lifecycle: cold runs record generated .pt2/.pte/.expected files, the workflow adds generated .pth files before cache save, cache hits reject missing manifest entries, and the generated SavedModel directories are checked separately. I did not find a new high-confidence correctness blocker in this revision.
This remains COMMENT / needs validation rather than an approval disposition for two reasons. First, exact-head CI is still incomplete: Test CUDA, Build C++, Build C library, and package/PyPI are green, while Test C++, Test Python, and CodeQL are still running. Second, because adding test_cc_local.sh to source_fingerprint changes the exact cache key for this head, the current C++ run is necessarily a cold-cache run; the DP_REUSE_GENERATED_PT_MODELS=1 cache-hit branch still needs a subsequent run on this same head to demonstrate the warm-cache path end to end.
The PR is authored by njzjz-bot, which is also the executing GitHub account, so a final APPROVE/REQUEST_CHANGES cannot be submitted by this account; once the validation above is complete, final approval must come from an independent reviewer/account.
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Follow-up on the same reviewed head now that the pending checks have finished: the exact-head Build C++, Test C++, Test Python, Test CUDA, Build C library, CodeQL, and package/PyPI workflows are all completed successfully. I rechecked the current head before submitting this disposition, and it remains bedaf3a3e322ad22822f569a5de23655303ac1ab.
I still would not treat the new cache behavior as fully validated yet. The Test C++ workflow for this head is run attempt 1, and this head changed the fixture source fingerprint/cache key by adding source/install/test_cc_local.sh. That means the successful run is the cold-cache population path; there is still no same-head PR-triggered Test C++ rerun demonstrating that the restored cache passes the manifest checks and actually exercises DP_REUSE_GENERATED_PT_MODELS=1 end to end. Since cache-hit reuse is the main behavior introduced by this PR, a warm rerun is the remaining validation I would want before an independent reviewer approves it.
No new code-level blocker was found beyond that validation gap. This PR is authored by the executing GitHub account (njzjz-bot), so this account also cannot provide the eventual independent APPROVE.
Resolve C++ test fixture caching conflict with upstream DPA4 spin LAMMPS tests.
Preserve DPA4SPIN_LMP_REQUIRED and generate gen_dpa4c_spin_lmp.py on cold caches.
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[P1] The merge at de33ebf4d163e7372dd46efb1a64485963a531fa drops the executable bit on source/install/test_cc_local.sh: its Git tree mode is 100644, whereas both parents (bedaf3a3 and 4e1479d8) have 100755. The workflow invokes this script directly, so it cannot reach fixture generation or C++ tests. Exact-head job 121689360451 confirms source/install/test_cc_local.sh: Permission denied and exit 126, with DP_REUSE_GENERATED_PT_MODELS=0.
Please restore the executable mode and rerun CI. This finding is scoped to the merge-resolution delta; it does not replace the earlier request for end-to-end warm-cache validation. No local C++ execution was performed.
Requesting changes for one remaining cache-invalidation gap: the source fingerprint omits an imported helper that directly changes generated model weights. A helper-only change can therefore restore an exact-key cache and skip regeneration, leaving the C++ tests on the previous models and reference results. The earlier omission of test_cc_local.sh is fixed; the inline finding concerns the separate shared fixture dependency.
Validation: I evaluated the exact fingerprint command with an isolated Git index initialized from this commit. Changing only the helper's jitter amplitude changed its output arrays but left the fingerprint unchanged; changing a listed generator changed the fingerprint as a control. I also exercised the exact generation script's cold/warm control flow with compiler/test/generator stand-ins: the cold generator list matches the baseline, a warm hit skips generation, and missing manifest, listed model, or SavedModel directory cases are rejected. Shell syntax, YAML parsing and whitespace checks passed.
This is source/fingerprint and controlled-flow validation, not a real AOTI export, compiled-artifact warm-load, GPU, full C++ test or hosted-CI qualification.
Merge current master while retaining corrupt-with-comm, canonical and
long-range generators within cold-cache generation and all failure waits.
Include imported generator helpers, native kernels/headers, build backend
and CMake configuration in the fixture fingerprint.
Validation: dependency-change and unrelated-test controls pass; an exact-shell
harness preserves master's CPU/CUDA generator lists, validates cold/warm
reuse and rejects partial caches and failed generation. Ruff 0.16, shell
syntax and whitespace checks pass. Full model generation remains for CI.
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Model: GPT-6 Astra Pro
Install dependencies before computing the native cache key. Include the
Python runtime and deterministically sorted installed distribution
versions, excluding DeePMD's commit-derived editable version because its
relevant inputs are already source-addressed. Retain exact-match restores.
Validation: execute the exact Bash fingerprint step against real temporary
metadata; dependency-version changes invalidate, editable commit-version
changes do not, and discovery order/repeated runs are stable. YAML ordering,
cache regression, shell syntax and whitespace checks pass.
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Model: GPT-6 Astra Pro
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Re-reviewed fe3b596 against my previous review. The omitted fixture-helper dependency is included, along with the relevant native sources, headers and CMake/configuration inputs. Runtime fingerprinting now happens after dependency installation and records Python and sorted resolved distribution versions, excluding only the editable project's commit-derived version whose relevant source is hashed separately. The script retains its executable mode and the current generator set and failure waits.
Validation: the committed fingerprint regression and its four subtests passed. A probe of the exact workflow Python payload confirmed dependency/Python-version invalidation, stable repeated/reordered discovery, and no invalidation from an editable DeePMD version-only change. With compiler and generator stand-ins, the exact shell script ran the expected 18 CPU / 20 CUDA generator invocations on cold paths, skipped all generators on warm paths, rejected missing manifest/model/SavedModel cases, and propagated a generator failure without producing a success manifest. Changed-file Ruff, shell syntax, workflow YAML and whitespace checks passed.
No remaining actionable source blocker found; approving. One validation limit remains: the successful current-head hosted C++ run exercised cold-cache population, not a real compiled-artifact warm restore. I recommend one same-head warm-cache run before merge to verify restored native fixture loading end to end. The orchestration probes above do not replace that check or constitute native/GPU execution.
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Summary
.pth,.pt2,.pte,.expected, and.savedmodelartifactsSelf-hosted runners
The regular C++ jobs in
deepmodelinguse thecpuself-hosted runner label. This is compatible with the cache design: heterogeneous runners are separated by the native-environment fingerprint, while a stable self-hosted machine should get better cache reuse. The compiler fingerprint is computed after the workflow's GCC installation step.Validation
git diff --checkpasses4/4CTest targets passed)retry_on_sigterm.shspecifically handles this hosted-runner transientCurrent cache fingerprint follow-up
Commit fe3b596 installs the Python environment before calculating/restoring the native fixture cache. The key includes the Python implementation/version and sorted installed distribution names/versions, excluding only the editable DeePMD distribution version whose relevant source is already hashed. Native CMake/config/library inputs and the DPA4 fixture helper also invalidate the source fingerprint.
Focused checks verify dependency-version invalidation, stable ordering/repeated execution, and no invalidation from an editable DeePMD version-only change. The exact-shell harness also verifies cold/warm cache control flow, fixture manifests and generator failure propagation. These checks validate cache orchestration; they do not substitute for completed native generation and CTest jobs.
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Model: GPT-6 Astra Pro
Summary by CodeRabbit
Tests
Chores