Deduplicate all files in the wheel cache by charliermarsh · Pull Request #21327 · astral-sh/uv

## Summary When content hashing is enabled, we currently allocate and zero a new 64 KiB buffer for every file we copy and hash during streaming extraction. This PR reuses one buffer across the wheel instead. For the PyTorch wheel used in the benchmarks, that reduces buffer allocations for hashing from 11,120 to one, while keeping the buffer size at 64 KiB per active wheel.

The following measurements compare #21327 at `a188b8e833aef3c3b4b60a32ed9fafe6ac74186a` with this optimization applied on top, before moving the change onto `main`. They are not measurements against `main`. The Linux benchmarks alternate base and candidate, using pinned wheels served over local HTTP with content-addressed caching enabled: | Cold install | #21327 | #21327 + buffer reuse | Change | | --- | ---: | ---: | ---: | | AnyIO | 110 ms | 107 ms | -2.6% | | SymPy | 845 ms | 775 ms | -8.3% | | NumPy | 627 ms | 567 ms | -9.5% | | PyTorch CPU | 6.50 s | 5.99 s | -7.8% | | 14-package environment, concurrency 4 | 6.95 s | 6.47 s | -7.0% | The individual results above use 16 paired rounds; the full environment uses 12.