task: review

Author: jharlow-intel

CHANGELOG.md

@@ -12,6 +12,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 * Multi-iterator constructors now return a status corresponding to allocation success or failure, and raise `MemoryError` instead of `ValueError` [gh-373](https://github.com/IntelPython/mkl_fft/pull/373)
 * `_direct_fftnd` now also checks the status returned by the backend instead of discarding it [gh-373](https://github.com/IntelPython/mkl_fft/pull/373)
 * Pinned Cython in the Coverity Scan workflow so generated code stays stable between scans, and added `coverity/README.md` documenting the known false-positive families and the scan review checklist [gh-374](https://github.com/IntelPython/mkl_fft/pull/374)
+* Reduced Python overhead in `norm="forward"`/`"ortho"` scaling by computing the scale factor without `numpy.prod` [gh-384](https://github.com/IntelPython/mkl_fft/pull/384)
 
 ### Fixed
 * Fixed `norm="forward"`/`"ortho"` scaling in `fftn`, `ifftn`, `rfftn`, `irfftn` and the `fft2` family when only a subset of axes is transformed: the scale used the full array shape instead of the transformed axes [gh-336](https://github.com/IntelPython/mkl_fft/issues/336), [gh-370](https://github.com/IntelPython/mkl_fft/pull/370)

mkl_fft/_fft_utils.py

@@ -72,12 +72,9 @@ def _compute_fwd_scale(norm, n, shape):
         return 1.0
 
     ss = n if n is not None else shape
-    # `np.prod` dominates the Python-side cost of a small normalized transform,
-    # so take cheaper routes for the two shapes the callers actually pass: a
-    # scalar `n` (1-D) and a sequence (`_nd_fwd_scale`). It stays the fallback
-    # because `numpy.fft` also accepts array-like `n` and `s` (e.g. a 0-d
-    # `n=np.array(8)`, a 1-D `s=np.array([4, 4])`), which `math.prod` cannot
-    # handle uniformly.
+    # Avoid np.prod's array-creation overhead on the hot scalar (1-D) and
+    # sequence (N-D) paths; np.prod stays as the fallback for array-like
+    # `n`/`s` (e.g. np.array(8)) that math.prod can't handle.
     if isinstance(ss, (int, np.integer)):
         nn = ss
     elif isinstance(ss, (list, tuple)):