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`e3nn` is declared only in the `dpa-adapt` extra, but
`sezm_nn/projection.py` imported it at module scope. That module is reached
from `deepmd/pt/model/descriptor/__init__.py`, so on an installation without
that extra `import deepmd.pt.model.model` raised
ModuleNotFoundError: No module named 'e3nn'
and the whole PyTorch backend was unusable, whether or not the user went
anywhere near a SeZM descriptor. `pip check` stays clean, because nothing
declares the dependency that the import actually creates.
Import `e3nn.o3` inside the two projection builders that need it instead, and
raise an ImportError naming the package and the extra when it is missing.
Both builders need it: the e3nn grid backend uses `ToS2Grid`/`FromS2Grid`,
and the Lebedev backend uses `spherical_harmonics`, so switching
`grid_method` is not a way to avoid the dependency and the message does not
suggest it.
Import timing and the error text are the only changes; no default, no
numerical behaviour and no public API is touched.
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Navigate logical layers of code changes, visualize relationships, and explore their blast radius. 📝 WalkthroughWalkthroughThe PyTorch SeZM projection module now imports ChangesOptional
Priority: ➖ Normal Estimated code review effort: 2 (Simple) | ~10 minutes Change: Bug fix · Severity of issue fixed: Medium Merge Risk: 🟡 Moderate · up to Run Ruff formatting before merging so the required CI check can pass. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
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Inline comments:
In `@deepmd/pt/model/descriptor/sezm_nn/projection.py`:
- Around line 61-66: Update the import handler around the SeZM function-space
nonlinearities to catch ModuleNotFoundError, translate it only when e.name
identifies the missing e3nn module, and re-raise all other import failures
unchanged. Preserve the existing user-facing installation message for the
missing e3nn case.
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Reviewed the complete current diff and existing discussion at this head. The lazy import fixes the unconditional PyTorch-backend dependency as intended. The existing unresolved inline finding is valid and I am not duplicating it: catching every ImportError can misreport an installed e3nn whose import fails because of a broken/transitive dependency as “e3nn is not installed”; the handler should only translate the missing-e3nn case and re-raise other import failures unchanged.
Disposition: COMMENT / NEEDS HUMAN REVIEW for now because the exact-head Test Python and Test C++ workflows are still in progress. The completed CUDA, C++ build, C-library build, package/PyPI, and CodeQL workflows are green.
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Codecov Report❌ Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## master #6036 +/- ##
==========================================
- Coverage 77.74% 77.50% -0.25%
==========================================
Files 1155 1155
Lines 139640 139649 +9
Branches 5056 5062 +6
==========================================
- Hits 108569 108230 -339
- Misses 29188 29537 +349
+ Partials 1883 1882 -1 ☔ View full report in Codecov by Harness. 🚀 New features to boost your workflow:
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Re-reviewed because the exact-head CI state has materially changed since the prior monitoring pass. All GitHub Actions workflows for this head are now completed successfully, so CI is no longer the reason to defer a final disposition.
The existing unresolved inline finding remains a concrete correctness/diagnostic blocker: _import_e3nn_o3() catches every ImportError, so an installed e3nn whose own import fails because of a broken/transitive dependency is incorrectly reported as “e3nn is not installed.” Please translate only the ModuleNotFoundError corresponding to the missing e3nn package and re-raise other import failures unchanged. I am not duplicating the already-precise inline comment.
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The docstrings added with the lazy import said e3nn is declared only in
the `dpa-adapt` extra. That is wrong. deepmd assembles extras through a
custom build backend, and backend/find_pytorch.py declares e3nn>=0.5.9
under the `torch` extra as well -- none of which appears in
pyproject.toml, which is all I had checked.
The import fix itself is unaffected: e3nn is still optional, and the
module-scope import still made it a hard requirement for importing
deepmd.pt.model.model. What changes is which installations that hurt.
It is not "an installation without the dpa-adapt extra" -- most installs
have e3nn -- but one that uses neither extra, such as the PyTorch CPU
install documented in doc/install/easy-install.md:
pip install torch --index-url https://download.pytorch.org/whl/cpu
pip install deepmd-kit
Comments only; no behaviour, API or test assertion changes.
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Re-reviewed the complete current diff because the head changed from the previously reviewed b1f6058e61c1564dd79333c1397951a664416a02 to this head. The follow-up changes correct the dependency wording in the docstrings/tests, but the existing correctness/diagnostic blocker is unchanged: _import_e3nn_o3() still catches every ImportError, so an installed e3nn whose own import (or a transitive dependency import) fails is incorrectly reported as “e3nn is not installed.” The existing exact-line review thread remains current and precise, so I am not duplicating it. Please translate only the missing-e3nn ModuleNotFoundError case and let unrelated import failures propagate.
Exact-head CI is not fully complete yet: Test CUDA is green, while Test Python, Test C++, Build C++, Build C library, CodeQL, and the package/PyPI workflow are still running. The REQUEST_CHANGES disposition is based on the concrete blocker above rather than the incomplete CI state.
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`_import_e3nn_o3` caught every ImportError and reported it as "e3nn is not installed". An e3nn that is installed but whose own import fails -- because something it imports is missing, or because the install is broken -- therefore sent the user to reinstall a package they already have, and hid the module that was actually missing. Catch ModuleNotFoundError and translate it only when it names `e3nn` itself; anything else propagates with its own traceback intact. A plain ImportError from inside a working e3nn (a renamed symbol, say) now propagates too, which is the same principle. Verified against the real import machinery rather than a constructed exception: with e3nn absent from the import path, the missing-package message is still what reaches the user, and the underlying cause's `name` is `e3nn`. The test that hides e3nn now does so by making the import fail through CPython (`sys.modules["e3nn"] = None`) instead of raising a ModuleNotFoundError of its own. The hand-built error carried no `name`, which is precisely the attribute that separates a missing package from a broken one, so it was not the error this code meets in practice.
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- 🪄 Fix CodeRabbit comments on this PR
🤖 Prompt to fix review comments
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Inline comments:
In `@source/tests/pt/test_optional_e3nn_import.py`:
- Around line 68-69: Format the ModuleNotFoundError call in
test_optional_e3nn_import.py according to Ruff’s standard multiline layout,
including trailing commas and indentation, without changing its behavior.
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Re-reviewed the complete current diff because the head changed from 5d3fa3a0610028c4f07b7555c8a0c00f28eb11c7 to 73656da4a8638baa1f1386c1cb2f3534be72fa4a. The previous correctness/diagnostic blocker is fixed: _import_e3nn_o3() now translates only a missing top-level e3nn ModuleNotFoundError and lets missing transitive dependencies propagate unchanged, with a focused regression test covering that case. I found no new high-confidence functional blocker in the current two-file diff.
Disposition: COMMENT / NEEDS HUMAN REVIEW for now because exact-head CI is not complete. Test CUDA, Build C++, and Build C library are green; Test Python, Test C++, CodeQL, and Build and upload to PyPI are still in progress. There is an existing formatter-only inline finding from CodeRabbit; I am not duplicating it or treating it as a functional blocker.
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The previously blocking import-error classification is fixed: only a genuinely missing top-level e3nn is translated into installation guidance, while failures from e3nn's own dependencies propagate with their original cause. The new subprocess regression covers that distinction. The reviewed head has not moved, and the exact-head Build C++, Build C library, Test CUDA, Test C++, Test Python, CodeQL, and PyPI/package workflows are all green. I found no remaining high-confidence blocker.
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Reviewed at 73656da. The premise holds: the documented PyTorch CPU install path (torch from the CPU index, then a bare pip install deepmd-kit) carries no extra and therefore no e3nn, and on the merge-base import deepmd.pt.model.model fails there. projection.py was the only module-scope e3nn import left under deepmd/. I ran the new subprocess tests with only projection.py reverted to the merge-base: 4 failed with the original ModuleNotFoundError; at head all 4 pass, and both branches of the helper (package absent, package present but a dependency missing) are exercised. CI: 12 Python shards and 4 C++ builds passed.
deepmd/pt/model/descriptor/sezm_nn/projection.pyimportse3nnat module scope, and that module isreached unconditionally from
deepmd/pt/model/descriptor/__init__.py.e3nnships with thetorchand
dpa-adaptextras, so most installs have it — but not the PyTorch CPU install thatdoc/install/easy-install.mddocuments, which deliberately uses no extra so that pip does not pull aCUDA build of torch:
On that documented path:
The whole PyTorch backend is unusable there, whether or not the user goes anywhere near a SeZM
descriptor, and the traceback points at a grid-projection helper rather than at a missing package.
Change
Import
e3nn.o3inside the two projection builders that need it, and raise anImportErrornamingboth the package and an extra that ships it when it is absent.
Both builders need it, so switching grid backends is not a way to avoid the dependency and the message
does not suggest it:
_build_e3nn_projection_matsToS2Grid,FromS2Grid_build_lebedev_projection_matsspherical_harmonicsImport timing and the error text are the only changes. No default, no numerical behaviour and no
public API is touched.
Tests
source/tests/pt/test_optional_e3nn_import.pyblockse3nnwith a meta-path hook in a subprocess(already-imported modules cannot be un-imported in-process) and checks that
deepmd.pt.model.modelanddeepmd.pt.entrypoints.mainboth import, and that building a projectorwithout
e3nnreports the package and the extra. Without the fix all three fail; with it all threepass.
source/tests/pt/model/test_descriptor_sezm_grid_projection.pyis unchanged by this PR and stillpasses (18 tests, 179 subtests), so the e3nn path itself is unaffected.
Summary by CodeRabbit
Bug Fixes
e3nnpackage.e3nnis unavailable.e3nnpackage now report their original error details instead of being misidentified as a missinge3nninstallation.Tests
e3nnimports, installation guidance, and dependency error handling.