MobileKernelBench: LLM이 모바일 디바이스용 효율적인 Kernel을 작성할 수 있을까?

# MoKA Accelerator 프롬프트 예시 (논문 Appendix B.3 기반)

accelerator_prompt = """
You are an expert in model deployment, proficient in PyTorch and C++ programming,
and familiar with the coding style of the MNN framework.
Your task is to analyse the performance bottlenecks of the following MNN operator code
and propose optimisation methods to accelerate it.

Then identify **exactly one** highest-impact speed bottleneck,
propose **exactly one** optimisation method and propose a modification plan.

Operator information: {op_info}
Current implementation: {code_book}
Current performance: {performance}
History optimisation info: {history_optmz_info}

Requirements:
- Return **one and only one** optimisation method -- the largest expected speedup.
- Keep fields brief; avoid lists of alternatives, disclaimers, or generic advice.
- Avoid the totally same optimizations that have already been attempted.

Output format (JSON):
{{
  "bottleneck": "<max 100 words>",
  "optimisation_method": "<max 100 words>",
  "modification_plan": "<max 100 words>"
}}
"""

# Debugger 컴파일 에러 프롬프트 예시
debugger_prompt = """
Operator information: {op_info}
Current implementation: {code_book}
Compilation errors: {compile_error}

Analyze the errors and provide suggestions.
Note:
- Only provide semantic suggestions (in text).
- For cross-file errors, refer to the relevant code snippets and adjust only the current code.
- Do NOT suggest modifications to other MNN framework files.

Output format (JSON):
{{
  "local_error_suggestion": [],
  "crossfile_error_suggestion": []
}}
"""