Ship the XNNPACK delegate as its own library - #21526
Open
shoumikhin wants to merge 65 commits into
Open
Conversation
Contributor
Author
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/21526
Note: Links to docs will display an error until the docs builds have been completed. ❌ 21 New Failures, 242 Pending, 1 Unrelated FailureAs of commit 78518c9 with merge base 6bbb75a ( NEW FAILURES - The following jobs have failed:
FLAKY - The following job failed but was likely due to flakiness present on trunk:
This comment was automatically generated by Dr. CI and updates every 15 minutes. |
This was referenced Jul 31, 2026
This was referenced Jul 31, 2026
shoumikhin
added a commit
that referenced
this pull request
Jul 31, 2026
The XNNPACK delegate runs many models on CPU, but it is compiled into whichever component links it. The wheel ships two copies today, one inside the Python bindings extension and another inside the training extension, and a C++ application cannot get the delegate at all without building it from source. Build the delegate as a shared library and ship it in the wheel, so a process has one copy and both the Python bindings and a C++ application can link the same one. XNNPACK and its microkernels are built as static libraries, so they are bundled inside this library rather than left for each consumer to supply. The runtime and the thread pool are resolved from their shared libraries instead of embedding another copy of either. Because the delegate now carries XNNPACK itself, the places that used to name those static libraries explicitly no longer do so when building shared, which would otherwise ship the same code twice. Registration still happens through a static initializer, and the delegate is retained on the link line so that initializer runs even though no symbol from it is referenced directly. This is gated on the existing `EXECUTORCH_BUILD_SHARED` option, so only the Linux wheel changes; iOS, Android, and embedded builds keep linking the static library exactly as before. Test plan: The wheel smoke test now asserts that exactly one shipped library defines the XNNPACK delegate, alongside the existing backend-registry, thread-pool, and CPU kernel assertions. The symbol it checks was confirmed to exist in the shipped libraries first, and the assertion was confirmed to fail against a wheel built before this change, where it correctly reports both copies by name. Built the wheel from a clean checkout and verified against a fresh virtual environment with a normal dependency-resolving install: - The wheel ships the delegate as its own versioned library next to the runtime, the thread pool, and the CPU kernels. - `nm -DC` across every shipped shared object shows exactly one definition of a representative delegate symbol, in the new library rather than in the two extensions that used to carry it. - The delegate still appears in the registered backend list at runtime, which confirms its static initializer still runs from the shared library. - `.pte` execution through the Python bindings is unchanged, with outputs matching eager PyTorch. - The backend-registry, thread-pool, and CPU kernel assertions all still hold with the new library loaded. - With `EXECUTORCH_BUILD_SHARED` off, the delegate remains a static library and no new shared object is produced, so every build that does not opt in is unaffected. ghstack-source-id: f241eb6 ghstack-comment-id: 5147561128 Pull-Request: #21526
This was referenced Aug 4, 2026
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
The XNNPACK delegate is what makes many models run fast on CPU. Today
it is compiled into whichever component links it, so the wheel carries two copies and a C++
application cannot use it at all without building from source.
How you use it
One detail worth knowing: a delegate registers itself when its library loads, and nothing in
your program calls into it directly. A normal link would therefore drop it as unused, so the
target carries the linker options that keep it. You do not have to do anything for that to
work, but it is why linking the target is enough and no explicit registration call is needed.
Tested
Built the wheel on Linux x86_64 and aarch64, installed it into a clean environment, and
confirmed by symbol inspection that exactly one shipped library defines the delegate. Linked a
C++ application against the component, confirmed the delegate survives the link and registers
itself, and ran a delegated model. Existing Python tests pass unchanged.