[ET-VK] Add fused HuggingFace RoPE operator (apply_rotary_emb_hf)

[SS-JIA]

Stack from ghstack (oldest at bottom):

• -> [ET-VK] Add fused HuggingFace RoPE operator (apply_rotary_emb_hf) #18592

Add a fused rotary positional embedding operator for the HuggingFace RoPE
convention used by Qwen3, Phi-4-mini, and other HF-based models.

The existing et_vk.apply_rotary_emb only matches the stock Meta/Llama RoPE
pattern (interleaved pairs via reshape+unbind+stack+flatten). HF models use a
different convention (split-half via slice+neg+cat), causing Qwen3's RoPE to
decompose into ~560 GPU dispatches per decode step instead of 16 fused
dispatches (~1,295 µs/decode, 7% of total).

This commit adds et_vk.apply_rotary_emb_hf with:

• Pattern matching: HfRotaryEmbeddingPattern in patterns/rope_hf.py using
  SubgraphMatcher to detect the HF RoPE graph and replace with fused op.
  Supports both full rotation (freqs_dim == head_dim) and partial rotation
  (freqs_dim < head_dim, e.g. Phi-4-mini with partial_rotary_factor=0.75)
  by registering two pattern variants in get_hf_rope_graphs().
• GLSL shader: rotary_embedding_hf.glsl which pairs elements at distance D/2
  (half-apart) instead of adjacent pairs, computing half_dim from the metadata
  UBO for dynamic shape support
• C++ dispatch: add_rotary_embedding_hf_node with corrected assertion
  (head_dim == freqs_dim, not freqs_dim*2) since HF freqs are full-dim
• Custom op registration in both xplat and fbcode
• Op tests covering multiple configurations and dynamic prefill→decode resize

Also adds a convert_phi4_mini_weights binary target to the phi_4_mini TARGETS
file to enable converting HF checkpoint weights to Meta format.

Authored with Claude.

Differential Revision: D98741178

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/18592

• 📄 Preview Python docs built from this PR

Note: Links to docs will display an error until the docs builds have been completed.

❌ 2 New Failures, 1 Cancelled Job, 2 Pending, 5 Unrelated Failures

As of commit 14f27e1 with merge base d7cc5d7 ():

NEW FAILURES - The following jobs have failed:

• Lint / lintrunner-mypy (gh)
• pull / test-multimodal-linux (gemma3-4b) / linux-job (gh)
  RuntimeError: Command docker exec -t 5b2562a1e4db9943608997c6d7c4c078882625e82666a00a72e7f4d553171369 /exec failed with exit code 139

CANCELLED JOB - The following job was cancelled. Please retry:

• Check Labels (gh)

FLAKY - The following jobs failed but were likely due to flakiness present on trunk:

• periodic / test-models-linux (buck2, mv3, portable, linux.2xlarge, 90) / linux-job (gh) (detected as infra flaky with no log or failing log classifier)
• pull / test-openvino-linux / linux-job (gh) (detected as infra flaky with no log or failing log classifier)
• pull / unittest / windows / windows-job (gh) (matched win rule in flaky-rules.json)
  ##[error]The operation was canceled.
• pull / unittest-editable / windows / windows-job (gh) (matched win rule in flaky-rules.json)
  ##[error]The operation was canceled.
• pull / unittest-wasm-bindings (--enable-etdump) / linux-job (gh) (detected as infra flaky with no log or failing log classifier)

This comment was automatically generated by Dr. CI and updates every 15 minutes.

[github-actions]

This PR needs a release notes: label

If your change should be included in the release notes (i.e. would users of this library care about this change?), please use a label starting with release notes:. This helps us keep track and include your important work in the next release notes.

To add a label, you can comment to pytorchbot, for example
@pytorchbot label "release notes: none"

For more information, see
https://github.com/pytorch/pytorch/wiki/PyTorch-AutoLabel-Bot#why-categorize-for-release-notes-and-how-does-it-work.