[AxelNoun]

Summary

_tensor_spec_to_evalue reads a constant's bytes by casting spec.storage.data_ptr() to a host pointer. When the storage is on CUDA, that pointer is a device address, so to_executorch() segfaults (exit 139) with no Python exception. This PR checks the storage device first and raises an InternalError, as @shoumikhin suggested in #21545, that names the device and says how to fix the export. As discussed in the issue, lowering a CUDA module onto the portable path is not supported; only the way it fails changes. The check applies only to non-empty constants that the emitter serializes itself and whose storage is not on CPU, which are exactly the cases that reach the host read of a device pointer.

Fixes #21545

Test plan

New test test_emit_non_cpu_constant_raises in exir/emit/test/test_emit.py. It uses meta storage, which is not host-readable either and needs no accelerator. Without the guard, that case already fails with a wrapped "NULL pointer access" InternalError, so the test matches on the device name in the message.

Built and installed from this branch after merging main at d6abe23 (pip install . --no-build-isolation on Ubuntu 24.04, Python 3.12, torch 2.14.0+cpu):

pytest exir/emit/test/test_emit.py

70 passed, 2 skipped, as before the merge. With _emitter.py replaced by main's, the new test fails on the "NULL pointer access" message. lintrunner --skip MYPY reports no issues on the two files.

I also ran the reproducer from #21545 on CUDA (RTX 3060 under WSL), with the same package and torch replaced by the cu130 build of the same version (2.14.0+cu130). With main's _emitter.py it segfaults in _tensor_spec_to_evalue (exit 139). With this branch it raises InternalError: ... Constant tensor storage is on device 'cuda:0', ... (exit 1).

I used Claude Code to write this PR.

cc @JacobSzwejbka @angelayi @nil-is-all @Gasoonjia @digantdesai

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