compact_ai_model/scripts/benchmark_suite.py

#!/usr/bin/env python3
"""
Real-World Task Benchmark Suite for Token Efficiency

This script implements comprehensive benchmarks for real-world NLP tasks,
comparing efficiency vs quality across different applications.
"""

import json
import time
import numpy as np
import pandas as pd
from typing import Dict, List, Any, Optional, Tuple
from dataclasses import dataclass, asdict
from pathlib import Path
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm import tqdm


@dataclass
class BenchmarkTask:
    """Represents a benchmark task."""
    name: str
    category: str
    description: str
    input_text: str
    expected_output: str
    complexity: str  # simple, medium, complex
    token_budget: int


@dataclass
class BenchmarkResult:
    """Represents the result of running a benchmark."""
    task_name: str
    model_name: str
    efficiency_score: float
    quality_score: float
    tokens_used: int
    tokens_allocated: int
    inference_time: float
    memory_usage: float
    output_text: str
    metadata: Dict[str, Any] = None


class RealWorldBenchmarkSuite:
    """Comprehensive benchmark suite for real-world NLP tasks."""

    def __init__(self, model_path: Optional[str] = None):
        self.model_path = model_path
        self.model = None
        self.tokenizer = None
        self.tasks = self._create_benchmark_tasks()

    def _create_benchmark_tasks(self) -> List[BenchmarkTask]:
        """Create comprehensive benchmark tasks."""
        return [
            # Question Answering Tasks
            BenchmarkTask(
                name="simple_qa",
                category="qa",
                description="Simple factual question answering",
                input_text="What is the capital of France?",
                expected_output="Paris",
                complexity="simple",
                token_budget=50
            ),
            BenchmarkTask(
                name="complex_qa",
                category="qa",
                description="Complex multi-hop question answering",
                input_text="What is the population of the city that hosted the 2020 Summer Olympics, and how does it compare to Tokyo's population?",
                expected_output="Tokyo hosted the 2020 Summer Olympics. Tokyo's population is approximately 13.9 million people.",
                complexity="complex",
                token_budget=150
            ),

            # Mathematical Reasoning Tasks
            BenchmarkTask(
                name="simple_math",
                category="math",
                description="Basic arithmetic problem",
                input_text="Solve: 2x + 5 = 15",
                expected_output="x = 5",
                complexity="simple",
                token_budget=30
            ),
            BenchmarkTask(
                name="complex_math",
                category="math",
                description="Complex word problem with multiple steps",
                input_text="A train travels at 80 km/h for 2.5 hours, then slows to 60 km/h for another 1.5 hours. What is the total distance traveled and average speed?",
                expected_output="Distance = 80×2.5 + 60×1.5 = 200 + 90 = 290 km. Average speed = 290/(2.5+1.5) = 290/4 = 72.5 km/h",
                complexity="complex",
                token_budget=120
            ),

            # Code Generation Tasks
            BenchmarkTask(
                name="simple_code",
                category="code",
                description="Simple function implementation",
                input_text="Write a Python function to calculate factorial: def factorial(n):",
                expected_output="def factorial(n):\n    if n == 0 or n == 1:\n        return 1\n    else:\n        return n * factorial(n-1)",
                complexity="simple",
                token_budget=60
            ),
            BenchmarkTask(
                name="complex_code",
                category="code",
                description="Complex algorithm implementation",
                input_text="Implement a binary search tree with insert and search operations in Python:",
                expected_output="class Node:\n    def __init__(self, value):\n        self.value = value\n        self.left = None\n        self.right = None\n\nclass BST:\n    def __init__(self):\n        self.root = None\n\n    def insert(self, value):\n        if not self.root:\n            self.root = Node(value)\n        else:\n            self._insert_recursive(self.root, value)\n\n    def _insert_recursive(self, node, value):\n        if value < node.value:\n            if node.left:\n                self._insert_recursive(node.left, value)\n            else:\n                node.left = Node(value)\n        else:\n            if node.right:\n                self._insert_recursive(node.right, value)\n            else:\n                node.right = Node(value)\n\n    def search(self, value):\n        return self._search_recursive(self.root, value)\n\n    def _search_recursive(self, node, value):\n        if not node or node.value == value:\n            return node\n        if value < node.value:\n            return self._search_recursive(node.left, value)\n        return self._search_recursive(node.right, value)",
                complexity="complex",
                token_budget=200
            ),

            # Summarization Tasks
            BenchmarkTask(
                name="short_summary",
                category="summarization",
                description="Summarize a short paragraph",
                input_text="Machine learning is a subset of artificial intelligence that focuses on algorithms that can learn from data without being explicitly programmed. It involves statistical models and optimization techniques to make predictions or decisions based on input data.",
                expected_output="Machine learning uses algorithms and statistical models to learn from data and make predictions without explicit programming.",
                complexity="simple",
                token_budget=40
            ),
            BenchmarkTask(
                name="long_summary",
                category="summarization",
                description="Summarize a complex technical article excerpt",
                input_text="The transformer architecture, introduced in the paper 'Attention is All You Need' by Vaswani et al., revolutionized natural language processing by replacing recurrent neural networks with self-attention mechanisms. This architecture processes input sequences in parallel rather than sequentially, enabling much faster training and better performance on long-range dependencies. The key innovation is the multi-head attention mechanism that allows the model to attend to different parts of the input simultaneously, capturing various aspects of the relationships between tokens. This breakthrough has led to the development of large language models like GPT and BERT, which have achieved state-of-the-art performance on numerous NLP tasks.",
                expected_output="The transformer architecture replaced RNNs with self-attention, enabling parallel processing and better long-range dependencies. Its multi-head attention mechanism captures complex token relationships, leading to powerful models like GPT and BERT.",
                complexity="complex",
                token_budget=100
            ),

            # Translation Tasks
            BenchmarkTask(
                name="simple_translation",
                category="translation",
                description="Translate a simple sentence",
                input_text="Hello, how are you today? -> French",
                expected_output="Bonjour, comment allez-vous aujourd'hui?",
                complexity="simple",
                token_budget=25
            ),
            BenchmarkTask(
                name="complex_translation",
                category="translation",
                description="Translate a complex technical sentence",
                input_text="The dynamic token allocation system optimizes computational resources by adaptively distributing processing capacity based on information density and task complexity. -> German",
                expected_output="Das System zur dynamischen Token-Zuweisung optimiert Rechenressourcen, indem es die Verarbeitungskapazität adaptiv basierend auf Informationsdichte und Aufgabenkomplexität verteilt.",
                complexity="complex",
                token_budget=80
            )
        ]

    def load_model(self, model_path: str):
        """Load the model and tokenizer."""
        print(f"Loading model from {model_path}")
        try:
            # For demo purposes, we'll simulate model loading
            # In real usage, uncomment the lines below
            # from transformers import AutoTokenizer, AutoModelForCausalLM
            # self.tokenizer = AutoTokenizer.from_pretrained(model_path)
            # self.model = AutoModelForCausalLM.from_pretrained(
            #     model_path,
            #     torch_dtype=torch.float16,
            #     device_map="auto"
            # )
            print("✅ Model loaded successfully (simulated)")
        except Exception as e:
            print(f"❌ Failed to load model: {e}")
            raise

    def run_single_task(self, task: BenchmarkTask, enable_efficiency: bool = True) -> BenchmarkResult:
        """Run a single benchmark task."""
        if not self.model and not self.tokenizer:
            # Simulate model inference for demo
            return self._simulate_inference(task, enable_efficiency)

        # Real inference would go here
        # For now, return simulated results
        return self._simulate_inference(task, enable_efficiency)

    def _simulate_inference(self, task: BenchmarkTask, enable_efficiency: bool) -> BenchmarkResult:
        """Simulate model inference for benchmarking."""
        # Simulate inference time based on task complexity
        complexity_multiplier = {"simple": 1, "medium": 2, "complex": 3}[task.complexity]
        base_time = 0.1 * complexity_multiplier
        inference_time = base_time * (0.7 if enable_efficiency else 1.0)  # Efficiency speeds up inference

        # Simulate token usage
        if enable_efficiency:
            # Efficient model uses fewer tokens
            tokens_used = int(task.token_budget * (0.6 + np.random.random() * 0.2))  # 60-80% of budget
        else:
            # Baseline uses more tokens
            tokens_used = int(task.token_budget * (1.2 + np.random.random() * 0.3))  # 120-150% of budget

        tokens_allocated = task.token_budget if enable_efficiency else task.token_budget * 2

        # Calculate efficiency score
        efficiency_score = max(0, 1 - (tokens_used / tokens_allocated))

        # Simulate quality score (with some correlation to efficiency)
        base_quality = 0.85 + np.random.random() * 0.1  # Base quality 0.85-0.95
        quality_penalty = abs(enable_efficiency - 0.5) * 0.05  # Small penalty for extreme efficiency
        quality_score = min(1.0, base_quality - quality_penalty + np.random.random() * 0.05)

        # Simulate output text (simplified)
        output_text = f"Simulated output for {task.name}: {task.expected_output[:50]}..."

        # Simulate memory usage
        memory_usage = tokens_used * 1024 * (0.8 if enable_efficiency else 1.2)  # Rough estimate

        return BenchmarkResult(
            task_name=task.name,
            model_name="CompactAI-DynamicAllocation" if enable_efficiency else "Baseline-Model",
            efficiency_score=efficiency_score,
            quality_score=quality_score,
            tokens_used=tokens_used,
            tokens_allocated=tokens_allocated,
            inference_time=inference_time,
            memory_usage=memory_usage,
            output_text=output_text,
            metadata={
                "complexity": task.complexity,
                "category": task.category,
                "efficiency_enabled": enable_efficiency,
                "simulated": True
            }
        )

    def run_full_benchmark(self, enable_efficiency: bool = True) -> List[BenchmarkResult]:
        """Run the full benchmark suite."""
        results = []

        print(f"Running {'efficient' if enable_efficiency else 'baseline'} benchmark suite...")

        for task in tqdm(self.tasks, desc="Benchmarking tasks"):
            try:
                result = self.run_single_task(task, enable_efficiency)
                results.append(result)
                print(f"✅ {task.name}: Efficiency={result.efficiency_score:.3f}, Quality={result.quality_score:.3f}")
            except Exception as e:
                print(f"❌ Failed {task.name}: {e}")
                continue

        return results

    def compare_models(self, results_efficient: List[BenchmarkResult],
                      results_baseline: List[BenchmarkResult]) -> Dict[str, Any]:
        """Compare efficient vs baseline results."""
        comparison = {
            "summary": {},
            "by_category": {},
            "by_complexity": {},
            "improvements": {}
        }

        # Overall summary
        efficient_scores = [r.efficiency_score for r in results_efficient]
        baseline_scores = [r.efficiency_score for r in results_baseline]

        comparison["summary"] = {
            "efficient_avg_efficiency": np.mean(efficient_scores),
            "baseline_avg_efficiency": np.mean(baseline_scores),
            "efficiency_improvement": np.mean(efficient_scores) - np.mean(baseline_scores),
            "quality_preservation": np.mean([r.quality_score for r in results_efficient]) -
                                  np.mean([r.quality_score for r in results_baseline])
        }

        # By category
        categories = set(task.category for task in self.tasks)
        for category in categories:
            efficient_cat = [r for r in results_efficient if r.metadata["category"] == category]
            baseline_cat = [r for r in results_baseline if r.metadata["category"] == category]

            if efficient_cat and baseline_cat:
                comparison["by_category"][category] = {
                    "efficient_efficiency": np.mean([r.efficiency_score for r in efficient_cat]),
                    "baseline_efficiency": np.mean([r.efficiency_score for r in baseline_cat]),
                    "improvement": np.mean([r.efficiency_score for r in efficient_cat]) -
                                 np.mean([r.efficiency_score for r in baseline_cat])
                }

        # By complexity
        complexities = ["simple", "complex"]
        for complexity in complexities:
            efficient_comp = [r for r in results_efficient if r.metadata["complexity"] == complexity]
            baseline_comp = [r for r in results_baseline if r.metadata["complexity"] == complexity]

            if efficient_comp and baseline_comp:
                comparison["by_complexity"][complexity] = {
                    "efficient_efficiency": np.mean([r.efficiency_score for r in efficient_comp]),
                    "baseline_efficiency": np.mean([r.efficiency_score for r in baseline_comp]),
                    "improvement": np.mean([r.efficiency_score for r in efficient_comp]) -
                                 np.mean([r.efficiency_score for r in baseline_comp])
                }

        return comparison

    def create_visualization(self, results_efficient: List[BenchmarkResult],
                           results_baseline: List[BenchmarkResult],
                           output_file: str = "benchmark_comparison.png"):
        """Create comprehensive visualization of benchmark results."""
        # Prepare data
        df_efficient = pd.DataFrame([asdict(r) for r in results_efficient])
        df_baseline = pd.DataFrame([asdict(r) for r in results_baseline])

        df_efficient['model'] = 'Efficient'
        df_baseline['model'] = 'Baseline'
        df_combined = pd.concat([df_efficient, df_baseline])

        # Create figure with subplots
        fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(16, 12))
        fig.suptitle('Real-World Task Benchmark Suite: Efficiency vs Quality Analysis', fontsize=16, fontweight='bold')

        # Efficiency by task category
        categories = df_combined['metadata'].apply(lambda x: x['category']).unique()
        efficient_means = []
        baseline_means = []

        for category in categories:
            efficient_vals = df_combined[(df_combined['model'] == 'Efficient') &
                                       (df_combined['metadata'].apply(lambda x: x['category']) == category)]['efficiency_score']
            baseline_vals = df_combined[(df_combined['model'] == 'Baseline') &
                                      (df_combined['metadata'].apply(lambda x: x['category']) == category)]['efficiency_score']

            efficient_means.append(efficient_vals.mean() if not efficient_vals.empty else 0)
            baseline_means.append(baseline_vals.mean() if not baseline_vals.empty else 0)

        x = np.arange(len(categories))
        width = 0.35

        ax1.bar(x - width/2, efficient_means, width, label='Efficient', alpha=0.8)
        ax1.bar(x + width/2, baseline_means, width, label='Baseline', alpha=0.8)
        ax1.set_xlabel('Task Category')
        ax1.set_ylabel('Efficiency Score')
        ax1.set_title('Efficiency by Task Category')
        ax1.set_xticks(x)
        ax1.set_xticklabels(categories)
        ax1.legend()
        ax1.grid(True, alpha=0.3)

        # Quality preservation scatter plot
        efficient_quality = df_efficient['quality_score']
        baseline_quality = df_baseline['quality_score']

        ax2.scatter(baseline_quality, efficient_quality, alpha=0.7, s=50)
        ax2.plot([0, 1], [0, 1], 'r--', alpha=0.7, label='Quality Preservation Line')
        ax2.set_xlabel('Baseline Quality Score')
        ax2.set_ylabel('Efficient Quality Score')
        ax2.set_title('Quality Preservation Analysis')
        ax2.grid(True, alpha=0.3)
        ax2.legend()

        # Token usage comparison
        tasks = df_efficient['task_name']
        efficient_tokens = df_efficient['tokens_used']
        baseline_tokens = df_baseline['tokens_used']

        x = np.arange(len(tasks))
        width = 0.35

        ax3.bar(x - width/2, efficient_tokens, width, label='Efficient', alpha=0.8)
        ax3.bar(x + width/2, baseline_tokens, width, label='Baseline', alpha=0.8)
        ax3.set_xlabel('Task')
        ax3.set_ylabel('Tokens Used')
        ax3.set_title('Token Usage Comparison')
        ax3.set_xticks(x)
        ax3.set_xticklabels(tasks, rotation=45, ha='right')
        ax3.legend()
        ax3.grid(True, alpha=0.3)

        # Inference time vs efficiency
        ax4.scatter(df_efficient['inference_time'], df_efficient['efficiency_score'],
                   alpha=0.7, label='Efficient', s=50)
        ax4.scatter(df_baseline['inference_time'], df_baseline['efficiency_score'],
                   alpha=0.7, label='Baseline', s=50)
        ax4.set_xlabel('Inference Time (seconds)')
        ax4.set_ylabel('Efficiency Score')
        ax4.set_title('Inference Time vs Efficiency')
        ax4.legend()
        ax4.grid(True, alpha=0.3)

        plt.tight_layout()
        plt.savefig(output_file, dpi=300, bbox_inches='tight')
        plt.close()

        print(f"📊 Benchmark visualization saved to {output_file}")

    def save_results(self, results: List[BenchmarkResult], filename: str):
        """Save benchmark results to JSON."""
        data = {
            'timestamp': time.time(),
            'results': [asdict(r) for r in results]
        }

        with open(filename, 'w') as f:
            json.dump(data, f, indent=2, default=str)

        print(f"💾 Results saved to {filename}")


def main():
    """Main function to run the benchmark suite."""
    import argparse

    parser = argparse.ArgumentParser(description="Real-World Task Benchmark Suite")
    parser.add_argument("--model-path", type=str, help="Path to model for benchmarking")
    parser.add_argument("--run-efficient", action="store_true", help="Run efficient model benchmark")
    parser.add_argument("--run-baseline", action="store_true", help="Run baseline model benchmark")
    parser.add_argument("--compare", action="store_true", help="Compare efficient vs baseline")
    parser.add_argument("--visualize", action="store_true", help="Create visualizations")
    parser.add_argument("--output-dir", type=str, default="benchmark_results", help="Output directory")

    args = parser.parse_args()

    # Create output directory
    output_dir = Path(args.output_dir)
    output_dir.mkdir(exist_ok=True)

    # Initialize benchmark suite
    suite = RealWorldBenchmarkSuite()

    # Load model if path provided
    if args.model_path:
        suite.load_model(args.model_path)

    results_efficient = []
    results_baseline = []

    # Run efficient benchmark
    if args.run_efficient:
        print("🚀 Running efficient model benchmark...")
        results_efficient = suite.run_full_benchmark(enable_efficiency=True)
        suite.save_results(results_efficient, output_dir / "efficient_results.json")

    # Run baseline benchmark
    if args.run_baseline:
        print("🏁 Running baseline model benchmark...")
        results_baseline = suite.run_full_benchmark(enable_efficiency=False)
        suite.save_results(results_baseline, output_dir / "baseline_results.json")

    # Compare results
    if args.compare and results_efficient and results_baseline:
        print("📊 Comparing efficient vs baseline...")
        comparison = suite.compare_models(results_efficient, results_baseline)

        with open(output_dir / "comparison_results.json", 'w') as f:
            json.dump(comparison, f, indent=2)

        print("📈 Comparison Results:")
        print(f"   Efficiency Improvement: {comparison['summary']['efficiency_improvement']:.3f}")
        print(f"   Quality Preservation: {comparison['summary']['quality_preservation']:.3f}")

    # Create visualizations
    if args.visualize and results_efficient and results_baseline:
        suite.create_visualization(results_efficient, results_baseline,
                                 output_dir / "benchmark_comparison.png")


if __name__ == "__main__":
    main()