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Do not delegate CoreML instance_norm with a rank 5 input - #23688
hemanth1999k wants to merge 2 commits into
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Core ML's instance_norm only accepts rank 3 and 4 inputs. InstanceNorm3d lowered fine, then failed to load: parameter x[0] has invalid rank 5 The partitioner now overrides support for instance_norm over a rank 5 input, so ops_to_not_decompose no longer keeps it whole. It decomposes into ops Core ML runs, and the model stays delegated and matches eager, including affine with batch size > 1. InstanceNorm1d/2d are unchanged. Fixes pytorch#11702
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| self.assertIn("executorch_call_delegate", op_names) | ||
| self.assertNotIn("aten.argmax.default", op_names) | ||
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| def test_instance_norm_3d_is_decomposed(self): |
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Can we test that rank 3/4 lower properly by testing the op is not in the lowered edge program, but executorch_call_delegate is, and then test rank 5 behaves differently.
See
self.assertIn("executorch_call_delegate", op_names)
self.assertNotIn("aten.argmax.default", op_names)
In the argmax example.
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Done. Rank 3 and rank 4 (with and without affine) now assert executorch_call_delegate is present, aten.instance_norm.default is not in the lowered edge program, and instance_norm is inside the delegate.
Rank 5 behaves differently: instance_norm is in neither the graph nor the delegate. It decomposes to _native_batch_norm_legit (+ view/repeat), which is still delegated, and the test asserts that. Without the partitioner change the rank 5 case fails these assertions, and on macOS it fails to load with "parameter x[0] has invalid rank 5".
Lower rank 3, 4 and 5 instance_norm and assert on the graph rather than only on ops_to_not_decompose: rank 3 / 4 instance_norm is delegated whole (a delegate call, no instance_norm left outside it), while rank 5 is decomposed, with no instance_norm in the graph or in the delegate, and the batch-norm based decomposition delegated.
Summary
Core ML's
instance_normonly accepts rank 3 and rank 4 inputs.InstanceNorm3dcurrently lowers to Core ML without complaint, then fails when the program loads:CoreMLPartitionernow overrides support forinstance_normwhen its input is rank 5.ops_to_not_decomposetherefore stops keeping it whole, it decomposes into ops Core ML runs, and the model stays delegated and matches eager.InstanceNorm1dandInstanceNorm2dare unchanged.Fixes #11702
Test plan
Added
test_instance_norm_3d_is_decomposedtobackends/apple/coreml/test/test_coreml_partitioner.py. It coversInstanceNorm3don (1, 3, 4, 4, 4) and an affineInstanceNorm3dwith batch size 2 on (2, 3, 4, 5, 6). For each it checks thatinstance_normis not inops_to_not_decompose, lowers withto_edge_transform_and_lower, runs the program throughexecutorch.runtime.Runtime, and compares with eager (atol/rtol 1e-2 because Core ML computes in fp16). It also checks thatInstanceNorm2dis still kept whole.13/13 pass with the change; on main only the new test fails. Run on macOS 26.2, executorch 1.5.1 / main, torch 2.14.0, coremltools 9.0.
ufmtandflake8are clean.cc @kimishpatel @YifanShenSZ @cymbalrush @metascroy @nil-is-all