Add end-to-end sampling controls to the LLM server worker protocol

[metascroy]

Summary

Add end-to-end sampling controls to the LLM server worker protocol.

• Plumb top_p, top_k, and seed from chat completion requests through the Python runtime and JSONL worker protocol.
• Extend the C++ worker sampling configuration while preserving existing defaults and greedy behavior.
• Validate sampling parameters at the API boundary and document their supported ranges and semantics.
• Add coverage for request validation, worker serialization, session propagation, and C++ protocol handling.

Test Plan

• Ran the focused Python LLM server test suite: 78 tests passed.
• Built the C++ worker integration successfully.
• Verified deterministic output for repeated seeded requests, different output for different or omitted seeds, and expected behavior for top_p and top_k.
• Verified invalid sampling values return HTTP 400 responses.

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/21561

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❌ 1 New Failure, 17 Pending

As of commit d91c02e with merge base 4b4df96 ():

NEW FAILURE - The following job has failed:

• pull / test-qnn-delegate-linux / linux-job (gh)
  RuntimeError: Command docker exec -t a8ab1180cba5423770a982a2beb61dc321794b162331b2d486d182fc98afc9f9 /exec failed with exit code 1

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[metascroy]

@claude review this code

[github-actions]

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