Introduce Judger Prompt Component

[kaiming-cheng]

This PR adds bottleneck analysis prompt building and response parsing to the diagnose module.

Core Components

1. BottleneckResult (judger_prompt.py)

Single dataclass representing a bottleneck analysis:

• Category: memory, compute, or underutilized
• Summary: one-line description
• Reasoning: explanation citing metrics
• Root_causes: list of causes with metric evidence
• Recommended_fixes: actionable fixes with rationale
• Configurable analysis:
  • num_bottlenecks: how many bottlenecks to identify (default: 2)
  • num_causes: root causes per bottleneck (default: 2)
  • num_fixes: fixes per bottleneck (default: 1)

2. Prompt Builder (judger_prompt.py)

Constructs structured LLM prompts from:

• Kernel source code
• NCU profiling metrics (formatted via metric_schema)
• Roofline analysis results (via ncu_roofline)
• GPU hardware specifications (via gpu_spec)

Example Usage

  prompt = build_bottleneck_prompt(                                                                                             
      kernel_code=kernel_src,                                                                                                   
      ncu_metrics=ncu_data,                                                                                                     
      roofline=roofline_result,                                                                                                 
      gpu_specs=gpu_specs,                                                                                                      
      num_bottlenecks=2,                                                                                                        
      num_causes=2,                                                                                                             
      num_fixes=1,                                                                                                              
  )                                                                                                                             
                                                                                                                                
  # After LLM call...              
   results = parse_bottleneck_response(llm_response)                     

More end-to-end testing in future PR