Perf(test): reuse random data across container types in quantile test

[aymuos15]

Description

test_many_elements_quantile allocated a fresh np.random.randn(elements) array — up to 17M elements (~130 MB) — inside the innermost loop, so the same large array was regenerated once for every (container type, element count, quantile-arg type) combination. The random values are irrelevant to the test (assertions only check the return type and that CPU/GPU results agree), so the repeated allocation and RNG fill added runtime without adding coverage.

This makes for the data generation to once per element count and reuses it across the container and argument types.

     @skip_if_quick
     def test_many_elements_quantile(self):  # pytorch#64947
-        for p in TEST_NDARRAYS:
-            for elements in (1000, 17_000_000):
+        for elements in (1000, 17_000_000):
+            data = np.random.randn(elements)
+            for p in TEST_NDARRAYS:
+                x = p(data)
                 for t in [*TEST_NDARRAYS, list]:
-                    x = p(np.random.randn(elements))
                     q = percentile(x, t([10, 50]))

Performance

Wall-clock of pytest tests/transforms/test_utils_pytorch_numpy_unification.py, median of 3 runs on the same machine:

	Median wall (3 runs)	Individual runs
Before	17.16s	18.36, 16.74, 17.16
After	12.01s	12.19, 11.99, 12.01

Machine: 12th Gen Intel Core i7-12800H (20 threads), Ubuntu 22.04.5 (kernel 6.8.0-124), Python 3.10.12, torch 2.10.0+cu128, CUDA available (NVIDIA RTX A1000 Laptop GPU).

Types of changes

• Non-breaking change (test-only; no functional code touched)
• Tests pass locally (pytest tests/transforms/test_utils_pytorch_numpy_unification.py)

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• tests/transforms/test_utils_pytorch_numpy_unification.py

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📝 Walkthrough

Walkthrough

The quantile test now generates one random NumPy array for each element count. It converts that array to each supported array type before checking percentile results.

Priority: ⬇️ Low

Estimated code review effort: 1 (Trivial) | ~3 minutes

Merge Risk: ⚪ Minimal · up to d6695

This test-only change reuses inputs across array types without an identified loss of test coverage; no actionable mergeability risk remains.

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 0.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 1 functions across 1 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly and concisely describes the main change: reusing random data across container types in the quantile test.
Description check	✅ Passed	The description explains the motivation, implementation, performance impact, test scope, and relevant environment. It omits the issue reference and several template checklist items, but it is otherwis…
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

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