Add Zig core with Python FFI for high-performance tensor operations #2
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Implements optional high-performance Zig core for tensor operations with Python FFI bindings. Library remains pure Python/JAX by default; Zig provides native performance when available.
Core Implementation
src/zig/core.zig): Matrix operations (multiply, transpose, trace), metric tensors (Minkowski, spherical polar), C ABI exportssrc/autograv/zig_ffi.py): ctypes bindings with NumPy integration, automatic library detection, graceful JAX fallbackbuild.zig): Cross-platform compilation, optimization levels, test suiteInfrastructure
.github/workflows/zig.yml): Multi-platform testing (Ubuntu, macOS, Windows), Zig 0.11.0/0.12.0, cross-compilation validationUsage
Architecture
Hybrid design: JAX for autodiff/GPU, optional Zig for performance-critical operations. Zig library usable standalone without Python. Foundation for future JAX/XLA custom operations.
Note: Building JAX/XLA with Zig as primary build system is impractical (complex C++ codebase with Bazel). Implementation focuses on Zig-based operations callable from Python, with documented path to XLA custom calls.
Security: CodeQL scan clean (0 alerts), workflow permissions scoped to
contents: read.Files: 15 modified, +2,879 lines (265 Zig, 489 Python, 1,609 docs, 417 build/CI, 99 config).
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esm.ubuntu.com/usr/lib/apt/methods/https /usr/lib/apt/methods/https(dns block)ziglang.org/usr/bin/curl curl -L REDACTED -o /tmp/zig.tar.xz(dns block)If you need me to access, download, or install something from one of these locations, you can either:
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