Reject CoreML delegation for unsupported input dtypes

[john-rocky]

Summary

CoreMLPartitioner queries coremltools.converters.mil.frontend.torch.is_torch_fx_node_supported
to decide whether a node can be delegated, but that function only inspects the op
name — it does not validate input dtypes. When a graph contains a node whose
inputs use a dtype outside TORCH_DTYPE_TO_MIL_DTYPE (e.g. torch.uint8 /
torch.int8), the partitioner happily tags it and the failure only surfaces
later from deep inside coremltools as a KeyError, aborting the whole export.

This change adds an explicit dtype check in should_override_support so such
nodes fall back to the portable backend, and wraps the support query in
try/except as a defensive measure for any other case where the call might
raise instead of returning False.

Fixes #11686.

Test plan

Added test_unsupported_dtype_does_not_crash_partitioner which lowers
torch.abs on a uint8 input via to_edge_transform_and_lower and asserts
the op is not delegated. Verified locally on macOS 15 / Python 3.10 /
coremltools 9.0:

$ python -m unittest -v executorch.backends.apple.coreml.test.test_coreml_partitioner.TestCoreMLPartitioner
Ran 9 tests in 104.215s

OK

Authored with Claude.

[pytorch-bot]

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⚠️ 12 Awaiting Approval

As of commit d324e22 with merge base 94d2881 ():

AWAITING APPROVAL - The following workflows need approval before CI can run:

• Apple (gh)
• Build documentation (gh)
• Build Presets (gh)
• Cadence Build & Test (gh)
• Lint (gh)
• periodic (gh)
• pull (gh)
• Test ARM Backend (gh)
• Test CoreML Backend (gh)
• Test QNN Backend (gh)
• Test Vulkan Backend (gh)
• Test XNNPACK Backend (gh)

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