Core ML: stop the gather opt-out from failing tied embeddings

[john-rocky]

Fixes #21856.

What happens today

op_linear_quantizer_config is applied with op_type_configs={"gather": None}, so that
embedding tables are not compressed by the linear quantizer. When a model ties its embedding
table to its output projection — one weight, two consumers — that constant reaches the
compressor configured two ways at once: quantized as a linear weight, exempt as a gather
table. coremltools refuses the conflict rather than resolving it:

ValueError: compression config conflict detected between ops
  %aten_embedding_default_...: gather(x=%p_out_weight_to_fp16, ...)
 and
  %aten_mm_default...: linear(x=..., weight=%p_out_weight_to_fp16, ...)
  ... has config None while ... has OpLinearQuantizerConfig(mode='LINEAR_SYMMETRIC', ...)

So the opt-out does not skip the table. It fails the whole lowering, and a very common LLM
shape cannot be lowered at all with coreml_quantize set.

The change

Gathers whose constant is also consumed by an op of another type are named in
op_name_configs with the same config as the op they share it with, so the constant is seen
one way. Every other gather keeps the opt-out, and a model whose MIL program is not available
returns no names, which leaves the behaviour exactly as it was.

Checking it

The new test in test_coreml_partitioner.py lowers a two-layer model twice, tied and untied.
Against main the tied case raises the error above; with this change both lower. The untied
case passes either way, which is the point — the opt-out still applies where it always did.

Measured on macOS arm64, coremltools 9.0.

[pytorch-bot]

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As of commit 2de6a15 with merge base 7c3cdbb ():

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