Summary

The Gemma 4 text decoder shipped with examples/models/gemma4/text_decoder/
already implements hybrid sliding/full attention, partial RoPE,
per-layer head_dim (256 for sliding / 512 for full), MQA, and YOCO
KV sharing in plain PyTorch.

I checked, and that implementation lowers cleanly through
torch.export and CoreMLPartitioner today — for the synthetic
10-layer Gemma 4 used in the new test, the lowered edge program
contains exactly executorch_call_delegate and getitem at the top
level (1186 MIL ops fully delegated). No portable fallbacks, no
unsupported ops.

So the missing piece is not new modeling code — it is the small amount
of glue that turns "exportable in principle" into "exportable from one
shell command". This PR adds that glue:

• examples/apple/coreml/gemma4/export_gemma4_text_decoder_coreml.py,
  with sensible CoreML defaults: iOS18+ deployment target so the
  YOCO KV caches can be taken over as stateful tensors,
  compute_unit=CPU_AND_NE, fp16 by default (the ANE requires fp16).
• A --random_weights mode for smoke-testing the export pipeline
  without a HuggingFace checkpoint, plus --config_json,
  --sliding_window, --sliding_window_pattern overrides.
• A readme.md documenting the flags and the "everything delegates"
  property.
• A BUCK target so the script is buildable in fbcode the same way
  the existing CoreML llama scripts are.

The audio and vision encoders are intentionally out of scope — the
existing ATen pipeline in examples/models/gemma4 is more appropriate
for those.

Test plan

examples/apple/coreml/gemma4/test.py builds a 10-layer synthetic
Gemma 4 (4 sliding + 1 full × 2) — same hybrid pattern as Gemma 4 E2B,
just at smaller dimensions — and runs the full export pipeline,
asserting the resulting .pte is non-empty.

$ python -m pytest examples/apple/coreml/gemma4/test.py -v
test.py::TestGemma4CoreMLExport::test_eager_forward_runs PASSED
test.py::TestGemma4CoreMLExport::test_full_export_pipeline_lowers_to_coreml PASSED
============================== 2 passed in 15.32s ==============================

I also ran the export by hand and confirmed the resulting edge program
is fully delegated.

Relationship to other open PRs

• Add --sliding_window flag to CoreML static LLM export #19250 / Add per-layer hybrid sliding/full attention (Gemma 3 / Gemma 4) to CoreML static LLM export #19251 add --sliding_window / --sliding_window_pattern
  for the static-LLM Llama path. Gemma 4's text decoder uses a
  different attention implementation (per-layer head_dim, partial
  RoPE, etc.) that already understands those concepts via Gemma4Config,
  so this PR doesn't depend on those — it just plumbs the equivalent
  overrides through to Gemma4Config directly.
• Add coreml_compute_plan.py: report which CoreML ops dispatch to ANE / GPU / CPU #19252 adds coreml_compute_plan.py, which is the natural next step
  for tuning a Gemma 4 export: run it against the produced .pte to
  see which ops the runtime would dispatch to the ANE vs the CPU.

Authored with Claude.