Add Metal backend support for Gemma3 runner

[seyeong-han]

Summary

Add make gemma3-metal build target and fix a CMake 4.0 build issue that breaks all Metal backend builds.

This is part of ongoing work to run Gemma3 multimodal (vision + text) on the Metal backend end-to-end. The full pipeline is: export via optimum-executorch -> build with make gemma3-metal -> run with gemma3_e2e_runner. The build and runner infrastructure in this PR works correctly. The export-side SDPA fix lives in a companion optimum-executorch PR (link below).

Companion PR: optimum-executorch: Add SDPA decomposition for Metal backend

Changes

1. make gemma3-metal build target

Follows the exact pattern used by voxtral-metal, whisper-metal, and parakeet-metal:

• examples/models/gemma3/CMakeLists.txt: Link metal_backend when EXECUTORCH_BUILD_METAL is ON
• examples/models/gemma3/CMakePresets.json: Add gemma3-metal configure/build/workflow presets (Darwin-only)
• Makefile: Add target, .PHONY, help text, update model description comment

2. Fix CMake 4.0 abseil C++17 detection (affects all Metal builds)

CMake 4.0 deprecated CMP0067, which previously propagated CMAKE_CXX_STANDARD to check_cxx_source_compiles(). Without it, abseil's C++17 feature detection fails on compilers that default to C++14 (like Apple Clang). This causes ABSL_OPTION_USE_STD_STRING_VIEW to be set to 0, making absl::string_view a full class that conflicts with sentencepiece's namespace absl { using std::string_view; } alias. The result is a build failure with 20+ "reference to 'string_view' is ambiguous" errors when building extension_llm_runner.

Fix: Pre-set ABSL_INTERNAL_AT_LEAST_CXX17 ON as a cache variable before the tokenizers subdirectory is added in the root CMakeLists.txt.

Current status and help needed

@manuelcandales
The pipeline works end-to-end (export, build, run), but the generated output is incorrect (gibberish tokens). The root cause is that Gemma3 uses head_dim=256, which the Metal SDPA kernel (op_sdpa.mm) does not support (it only handles 64, 96, 128). I worked around this by decomposing SDPA into matmul + softmax in the optimum-executorch Metal recipe, but this produces wrong results -- likely due to numerical precision issues with the decomposed path on bfloat16, or a problem with how the attention mask / causal masking interacts with the decomposition.

I'd appreciate guidance on:

• Is adding head_dim=256 template instantiation to the Metal SDPA kernel (op_sdpa.mm) feasible? That would be the ideal fix instead of decomposition.
• Are there known numerical issues with bfloat16 matmul on MPS that could cause the decomposed SDPA to produce garbage?
• Any other suggestions for getting Gemma3 (head_dim=256) working correctly on Metal.

Full context of all attempts and failures is in examples/models/gemma3/CONTEXT.md.

Test plan

# Build
make gemma3-metal

# Export (requires companion optimum-executorch PR)
optimum-cli export executorch \
  --model "google/gemma-3-4b-it" \
  --task "multimodal-text-to-text" \
  --recipe "metal" \
  --dtype bfloat16 \
  --device mps \
  --output_dir="gemma3/gemma3-4b-it-metal"

# Run
./cmake-out/examples/models/gemma3/gemma3_e2e_runner \
  --model_path gemma3/gemma3-4b-it-metal/model.pte \
  --tokenizer_path <path-to>/gemma3_tokenizer.json \
  --image_path docs/source/_static/img/et-logo.png \
  --temperature 0

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[pytorch-bot]

🔗 Helpful Links

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