localgenai/testing/qwen3-coder-30b/llm_test_framework_simple.py

#!/usr/bin/env python3
"""
LLM Performance Testing Framework (Simplified Version)
"""

import time
import json
import os
from abc import ABC, abstractmethod
from typing import Dict, List, Any, Tuple
from dataclasses import dataclass
from pathlib import Path

@dataclass
class TestResult:
    """Container for test results"""
    model_name: str
    task_name: str
    response_time: float
    tps: float
    cpu_usage: float
    memory_usage: float
    quality_score: float
    raw_output: str
    expected_output: str
    success: bool
    timestamp: float

@dataclass
class PerformanceMetrics:
    """Container for performance metrics"""
    response_time: float
    tps: float
    cpu_avg: float
    memory_avg: float

class LLMInterface(ABC):
    """Abstract base class for LLM interfaces"""
    
    def __init__(self, model_name: str, config: Dict[str, Any]):
        self.model_name = model_name
        self.config = config
    
    @abstractmethod
    def generate(self, prompt: str, **kwargs) -> str:
        """Generate text from prompt"""
        pass
    
    @abstractmethod
    def get_model_info(self) -> Dict[str, Any]:
        """Get model information"""
        pass

class TestSuite:
    """Main test suite class"""
    
    def __init__(self, output_dir: str = "test_results"):
        self.output_dir = Path(output_dir)
        self.output_dir.mkdir(exist_ok=True)
        self.results: List[TestResult] = []
        self.models: List[LLMInterface] = []
    
    def add_model(self, model: LLMInterface):
        """Add a model to test"""
        self.models.append(model)
    
    def run_single_test(self, model: LLMInterface, task: Dict[str, Any]) -> TestResult:
        """Run a single test on a model"""
        # Start timing
        start_time = time.time()
        
        # Simulate resource usage
        cpu_start = 50  # Percentage (simulated)
        memory_start = 1024  # MB (simulated)
        
        # Generate response
        try:
            response = model.generate(task["prompt"], **task.get("parameters", {}))
            success = True
        except Exception as e:
            response = f"Error: {str(e)}"
            success = False
        
        # Measure completion
        end_time = time.time()
        response_time = end_time - start_time
        
        # Calculate TPS (simulated)
        tps = 0
        if success and response:
            # Estimate tokens (very rough approximation)
            tps = len(response.split()) / response_time if response_time > 0 else 0
        
        # Simulate end resources
        cpu_end = 60  # Percentage (simulated)
        memory_end = 1536  # MB (simulated)
        
        # Calculate averages
        cpu_avg = (cpu_start + cpu_end) / 2
        memory_avg = (memory_start + memory_end) / 2
        
        # Quality score - this is a placeholder
        quality_score = self._calculate_quality_score(response, task.get("expected_output", ""))
        
        result = TestResult(
            model_name=model.model_name,
            task_name=task["name"],
            response_time=response_time,
            tps=tps,
            cpu_usage=cpu_avg,
            memory_usage=memory_avg,
            quality_score=quality_score,
            raw_output=response,
            expected_output=task.get("expected_output", ""),
            success=success,
            timestamp=time.time()
        )
        
        self.results.append(result)
        return result
    
    def run_all_tests(self, tasks: List[Dict[str, Any]]):
        """Run all tests across all models"""
        for model in self.models:
            print(f"Running tests on {model.model_name}")
            for task in tasks:
                result = self.run_single_test(model, task)
                print(f"  {task['name']}: {result.response_time:.2f}s, {result.quality_score:.2f}/100")
    
    def _calculate_quality_score(self, response: str, expected: str) -> float:
        """Calculate quality score based on response"""
        # This is a placeholder implementation
        # In practice, this could use static analysis, code execution, etc.
        if not response:
            return 0.0
            
        # Very basic quality scoring
        score = 50  # Base score
        
        # Add points for presence of expected patterns
        if expected and expected.lower() in response.lower():
            score += 20
            
        # Add points for valid syntax (simplified)
        if not response.startswith("Error:") and len(response) > 10:
            score += 10
            
        # Cap score at 100
        return min(score, 100.0)
    
    def save_results(self, filename: str = None):
        """Save test results to JSON file"""
        if filename is None:
            filename = f"results_{int(time.time())}.json"
        
        results_data = []
        for result in self.results:
            results_data.append({
                "model_name": result.model_name,
                "task_name": result.task_name,
                "response_time": result.response_time,
                "tps": result.tps,
                "cpu_usage": result.cpu_usage,
                "memory_usage": result.memory_usage,
                "quality_score": result.quality_score,
                "raw_output": result.raw_output,
                "expected_output": result.expected_output,
                "success": result.success,
                "timestamp": result.timestamp
            })
        
        with open(self.output_dir / filename, 'w') as f:
            json.dump(results_data, f, indent=2)
        
        print(f"Results saved to {self.output_dir / filename}")

# Example model implementations
class MockLLM(LLMInterface):
    """Mock LLM for testing purposes"""
    
    def generate(self, prompt: str, **kwargs) -> str:
        # Simulate generation time
        time.sleep(0.1)
        return f"Mock response for: {prompt}"
    
    def get_model_info(self) -> Dict[str, Any]:
        return {
            "name": self.model_name,
            "type": "mock",
            "version": "1.0"
        }

class ExampleTask:
    """Example task class for testing"""
    
    @staticmethod
    def get_sample_tasks() -> List[Dict[str, Any]]:
        return [
            {
                "name": "Simple function",
                "prompt": "Write a Python function to add two numbers",
                "expected_output": "return a + b",
                "parameters": {}
            },
            {
                "name": "Loop example",
                "prompt": "Write a Python loop that prints numbers 1 to 10",
                "expected_output": "for i in range(1, 11)",
                "parameters": {}
            }
        ]

if __name__ == "__main__":
    # Create test suite
    suite = TestSuite()
    
    # Add mock models
    suite.add_model(MockLLM("mock1", {}))
    suite.add_model(MockLLM("mock2", {}))
    
    # Run sample tests
    tasks = ExampleTask.get_sample_tasks()
    suite.run_all_tests(tasks)
    
    # Save results
    suite.save_results()
    
    print("Testing completed!")