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macOS Python wheel _portable_lib has non-relocatable libtorch rpath

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Dal 13/10/2026.

Valutazione

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Descrizione

Summary

The macOS ExecuTorch nightly wheel installs executorch.extension.pybindings._portable_lib, but its Mach-O rpath points at a CI-only Torch directory. On a normal virtual environment, importing the module fails because @rpath/libtorch_python.dylib cannot be resolved.

It can be worked around with

DYLD_FALLBACK_LIBRARY_PATH="$VIRTUAL_ENV/lib/python3.13/site-packages/torch/lib${DYLD_FALLBACK_LIBRARY_PATH:+:$DYLD_FALLBACK_LIBRARY_PATH}" python -c 'import
  │ executorch.extension.pybindings._portable_lib; print("portable pybind runtime loaded")'

Environment

  • macOS
  • Python 3.13.3
  • executorch==1.4.0.dev20260818
  • torch==2.13.0

Reproduction

python -c "import executorch.extension.pybindings._portable_lib"

The import fails with:

dlopen(.../_portable_lib.cpython-313-darwin.so, 0x0002): Library not loaded: @rpath/libtorch_python.dylib
Reason: tried: '/Users/runner/work/_temp/conda_environment_32133036333/lib/python3.13/site-packages/torch/lib/libtorch_python.dylib' (no such file),

Evidence

otool -L venv/lib/python3.13/site-packages/executorch/extension/pybindings/_portable_lib.cpython-313-darwin.so
otool -l venv/lib/python3.13/site-packages/executorch/extension/pybindings/_portable_lib.cpython-313-darwin.so

The extension links against:

@rpath/libtorch_python.dylib
@rpath/libtorch.dylib
@rpath/libtorch_cpu.dylib
@rpath/libc10.dylib

but contains only this rpath:

Load command 25
          cmd LC_RPATH
      cmdsize 112
         path /Users/runner/work/_temp/conda_environment_32133036333/lib/python3.13/site-packages/torch/lib (offset 12)

That build-machine path does not exist for wheel users.

Expected behavior

The wheel should be relocatable: _portable_lib should locate the libtorch*.dylib files supplied by the installed Torch wheel, for example via an appropriate relative @loader_path/@rpath installation layout or a repaired wheel rpath.

Actual behavior

The native runtime cannot load, so downstream packages cannot register native backends against it. The failure can be easy to miss when callers catch OSError to keep their Python package importable; backend availability then fails later at model load.

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