Resolve host vs target num kernels issue for product et builds #14365
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/14365
Note: Links to docs will display an error until the docs builds have been completed. ❌ 2 New FailuresAs of commit 9f47d6d with merge base c9f46e2 ( NEW FAILURES - The following jobs have failed:
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…ch#14365) Summary: Applying the max kernel num setting through a config/mode results in both host and target builds to limit number of kernels. This can result in a case where the host tooling is building with all ops/kernels but has the setting applied causing crashes on load. Host doen't (usually) have such memory limitation as target so adding select to disable the application of max kernel num when applying a host toolchain. Differential Revision: D82579896
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…ch#14365) Summary: Applying the max kernel num setting through a config/mode results in both host and target builds to limit number of kernels. This can result in a case where the host tooling is building with all ops/kernels but has the setting applied causing crashes on load. Host doen't (usually) have such memory limitation as target so adding select to disable the application of max kernel num when applying a host toolchain. Reviewed By: larryliu0820 Differential Revision: D82579896
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Differential Revision: D82579896 Pull Request resolved: pytorch#14365
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Summary:
Applying the max kernel num setting through a config/mode results in both host and target builds to limit number of kernels. This can result in a case where the host tooling is building with all ops/kernels but has the setting applied causing crashes on load.
Host doen't (usually) have such memory limitation as target so adding select to disable the application of max kernel num when applying a host toolchain.
Differential Revision: D82579896