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Don't cache source partitions #15541
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/15541
Note: Links to docs will display an error until the docs builds have been completed. ❌ 8 New Failures, 4 Unrelated FailuresAs of commit 32b4794 with merge base cadb0db ( NEW FAILURES - The following jobs have failed:
BROKEN TRUNK - The following jobs failed but were present on the merge base:👉 Rebase onto the `viable/strict` branch to avoid these failures
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Thanks for adding this. FYI We've deprecated the to_edge + to_backend flow for XNNPACK and will throw up a big warning when used. Fixing the caching issue is probably good regardless, even if it mostly only affects to_edge + to_backend (we preserve linear w/ to_edge_transform_and_lower).
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Summary
This caching breaks multi-method; for the second method, we are using the graph of the first method, and do not delegate properly. I think this is because the source partitions are based on the first graph, so the addmm nodes do not match up/do not give a partition, so the graph doesn't get tagged by the partitioner.
See issue: #12804
In the example from the issue, the first method
fwdis delegated well, and the second methodfwd_2does not have the linear functions delegated (it is using portable kernels, and the weights are duplicated in the constant segment).Also add test_partitioner to buck targets.
Test plan
python -m unittest backends.xnnpack.test.test_xnnpack_partitioner