Repository navigation
Fix CP norm for complex weights - #656
Merged
Merged
Conversation
Codecov Report❌ Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #656 +/- ##
==========================================
+ Coverage 90.71% 91.86% +1.15%
==========================================
Files 132 132
Lines 8056 8446 +390
==========================================
+ Hits 7308 7759 +451
+ Misses 748 687 -61 ☔ View full report in Codecov by Harness. 🚀 New features to boost your workflow:
|
aarmey
approved these changes
Oct 11, 2026
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
cp_normandCPTensor.norm()can return an imaginary value for a tensor with complex CP weights. For a weight of1jand real factors[[1], [2]]and[[3], [4]], both return11.180339887498949j, while the represented tensor has Frobenius norm11.180339887498949.Conjugate the second weight axis in the weight outer product, extending the complex-factor support introduced by @aarmey in #423 to complex weights. The squared norm is real, so take its real part before the square root. This also supports Paddle CPU, whose complex
sqrtkernel is unavailable.Expose the backend
realoperation through the existing dispatch mechanism. Register native operations for the dense backends and NumPy sparse backend. Paddle returns an already-real input unchanged because its nativerealkernel accepts complex tensors only. Add scalar/vector real-part tests for float32/float64/complex64/complex128, norm output dtype checks, and float32/float64 weighted and unweighted controls.Local validation on Python 3.12.14, with the core tensor algebra:
realoperation covers real and complex input dtypes. This run is bound to the 12 modified core files; the later sparse-only registration is covered by the NumPy complete suite and the eight sparse controls above.realwrappers and real-valued norm projection gives 8 failures and 8 controls for the 16 complex-weight cases on Paddle; all 16 pass with the corrected formula. This is a controlled comparison over the exact base archive, with those explicit overlays.fast=Falsesafety checks; every file is unchanged. The CLI traversal attempts did not complete.These validation ranges overlap. JAX, TensorFlow and CuPy bindings were inspected statically; their runtime suites were not run locally. Existing Windows test dependencies were reused, and the isolated Paddle environment uses Python 3.12.14 while official CI currently uses 3.12.15. GPU, model accuracy and performance tests were not run. Test tolerances, skip conditions and CI configuration are unchanged.
Fixes #655.