iGavroche-rocm-ninodes/create_optimization_plan.py

#!/usr/bin/env python3
"""
Optimization plan and implementation for ROCMOptimizedVAEDecode
Based on baseline performance analysis and gfx1151 architecture optimization
"""
import json
import os
from pathlib import Path

def create_optimization_plan():
    """Create a comprehensive optimization plan for ROCMOptimizedVAEDecode"""
    
    # Load baseline metrics
    with open("test_data/optimization/baseline_metrics.json", "r") as f:
        baseline = json.load(f)
    
    optimization_plan = {
        "node_name": "ROCMOptimizedVAEDecode",
        "baseline_performance": baseline,
        "optimization_targets": [
            {
                "name": "Memory Management Optimization",
                "description": "Optimize memory allocation and deallocation patterns for gfx1151",
                "target_improvement": "20-30%",
                "techniques": [
                    "Implement memory pooling for frequent allocations",
                    "Optimize tensor memory layout for ROCm",
                    "Reduce memory fragmentation",
                    "Implement smart caching for intermediate results"
                ]
            },
            {
                "name": "Tile Size Optimization",
                "description": "Find optimal tile sizes for gfx1151 architecture",
                "target_improvement": "15-25%",
                "techniques": [
                    "Profile different tile sizes (256, 512, 768, 1024, 1280)",
                    "Implement adaptive tile sizing based on input dimensions",
                    "Optimize overlap calculations",
                    "Cache optimal tile configurations"
                ]
            },
            {
                "name": "Precision Optimization",
                "description": "Optimize precision modes for gfx1151 performance",
                "target_improvement": "10-20%",
                "techniques": [
                    "Implement fp16 accumulation where beneficial",
                    "Optimize autocast usage",
                    "Reduce precision overhead",
                    "Implement mixed precision strategies"
                ]
            },
            {
                "name": "Batch Processing Optimization",
                "description": "Optimize batch processing for AMD GPUs",
                "target_improvement": "25-35%",
                "techniques": [
                    "Implement optimal batch sizes for gfx1151",
                    "Optimize memory bandwidth utilization",
                    "Implement batch-aware memory management",
                    "Optimize parallel processing patterns"
                ]
            },
            {
                "name": "Video Processing Optimization",
                "description": "Optimize video chunk processing for temporal consistency",
                "target_improvement": "30-40%",
                "techniques": [
                    "Implement optimal video chunk sizes",
                    "Optimize temporal overlap handling",
                    "Implement video-specific memory patterns",
                    "Optimize frame-to-frame processing"
                ]
            }
        ],
        "optimization_strategy": {
            "phase_1": {
                "name": "Memory and Tile Optimization",
                "priority": "high",
                "estimated_improvement": "25-35%",
                "implementation_order": [
                    "Memory pooling implementation",
                    "Tile size profiling and optimization",
                    "Memory layout optimization"
                ]
            },
            "phase_2": {
                "name": "Precision and Batch Optimization",
                "priority": "medium",
                "estimated_improvement": "20-30%",
                "implementation_order": [
                    "Precision mode optimization",
                    "Batch processing optimization",
                    "Mixed precision implementation"
                ]
            },
            "phase_3": {
                "name": "Video and Advanced Optimizations",
                "priority": "medium",
                "estimated_improvement": "30-40%",
                "implementation_order": [
                    "Video chunk optimization",
                    "Advanced memory patterns",
                    "Performance monitoring integration"
                ]
            }
        },
        "success_metrics": {
            "performance_targets": {
                "average_execution_time": "< 0.0003s (30% improvement)",
                "median_execution_time": "< 0.00014s (30% improvement)",
                "max_execution_time": "< 0.00075s (30% improvement)",
                "standard_deviation": "< 0.00028s (30% improvement)"
            },
            "memory_targets": {
                "memory_efficiency": "> 90%",
                "memory_fragmentation": "< 5%",
                "cache_hit_rate": "> 85%"
            },
            "quality_targets": {
                "output_quality": "No degradation",
                "numerical_stability": "Maintained",
                "compatibility": "Full ComfyUI compatibility"
            }
        }
    }
    
    # Save optimization plan
    os.makedirs("test_data/optimization", exist_ok=True)
    with open("test_data/optimization/optimization_plan.json", "w") as f:
        json.dump(optimization_plan, f, indent=2)
    
    print("=== ROCMOptimizedVAEDecode Optimization Plan ===")
    print(f"Baseline Performance: {baseline['average_time']:.6f}s average")
    print(f"Target Performance: < 0.0003s average (30% improvement)")
    print(f"Optimization Phases: 3 phases with incremental improvements")
    print(f"Total Estimated Improvement: 50-70%")
    print(f"\nOptimization plan saved to test_data/optimization/optimization_plan.json")
    
    return optimization_plan

if __name__ == "__main__":
    create_optimization_plan()