PyTorch: [Windows] Depend on PyPI CUDA runtime packages in wheels (#194056)
Summary This PR teaches Windows PyTorch CUDA wheels to locate their CUDA runtime DLLs from installed NVIDIA PyPI packages (via cuda-pathfinder) rather than bundling them inside torch/lib/.
Executive Summary
Summary This PR teaches Windows PyTorch CUDA wheels to locate their CUDA runtime DLLs from installed NVIDIA PyPI packages (via cuda-pathfinder) rather than bundling them inside torch/lib/. It adds per-OS wheel dependency metadata, updates the build matrix generator, adjusts CMake to skip bundling when using PyPI-sourced runtimes, and adds a Windows smoke test that validates the declared cuda-toolkit version matches the build.
Hyperscale Open Source & Cloud Infrastructure Review
From an infrastructure engineering, cloud-native scale, and hyperscale governance standpoint: - **Hyperscale Provenance:** Open-sourcing internal frameworks subjects proprietary systems to rigorous public analysis. - **De-facto Standard Cohesion:** Publishing enterprise tooling establishes interoperable specifications across distributed execution. - **Vendor Decoupling:** Platform engineers can inspect underlying telemetry and memory layouts without black-box vendor lock-in.
Impact on the Open Ecosystem
Bridges enterprise engineering scale with independent, reproducible open-source software stacks.