#!/usr/bin/env python3
"""
Initial attention optimization program for AlphaEvolve reproduction.
This program defines MLIR transformation parameters that will be evolved.
Targets 32% speedup like the original AlphaEvolve paper.
"""

import json
import sys
import random

def optimize_attention():
    """
    Define attention optimization parameters for evolution.
    
    The goal is to achieve 32% speedup (1.32x) like AlphaEvolve paper
    by optimizing compiler-generated MLIR IR for attention kernels.
    """
    
    # AlphaEvolve-inspired parameter space exploration
    # These parameters control MLIR compiler transformations
    
    # Memory tiling strategy - crucial for cache performance  
    # Based on typical L1/L2 cache sizes and attention patterns
    tile_options_m = [16, 32, 64, 128]  # Sequence dimension tiles
    tile_options_n = [32, 64, 128, 256] # Head dimension tiles
    
    # Smart initialization: favor cache-friendly sizes
    tile_size_m = random.choice([32, 64])  # Sweet spot for most caches
    tile_size_n = random.choice([64, 128]) # Head dim optimization
    
    # Vectorization strategy - critical for modern SIMD
    vectorization_options = ['none', 'affine', 'linalg']
    vectorization = random.choice(vectorization_options)
    
    # Loop unrolling - balance code size vs performance
    unroll_factors = [1, 2, 4, 8]
    # Favor moderate unrolling for attention kernels
    unroll_factor = random.choice([2, 4] if random.random() > 0.5 else unroll_factors)
    
    # Fusion strategy - key for reducing memory traffic
    fusion_strategies = ['none', 'producer', 'consumer', 'both']
    # Favor fusion for attention (Q@K^T, softmax, @V pattern)
    fusion_strategy = random.choice(['both', 'producer'] if random.random() > 0.3 else fusion_strategies)
    
    # Loop interchange - can improve memory access patterns
    loop_interchange = random.choice([True, False])
    
    # Memory optimizations - crucial for large attention matrices
    use_shared_memory = random.choice([True, False])
    
    # Performance vs latency trade-off
    optimize_for_latency = random.choice([True, False])
    
    # Additional optimizations inspired by FlashAttention
    enable_blocking = random.choice([True, False])  # Block-wise computation
    enable_recomputation = random.choice([True, False])  # Memory vs compute trade-off
    
    optimization_params = {
        # Core tiling parameters
        'tile_size_m': tile_size_m,
        'tile_size_n': tile_size_n,
        
        # Vectorization and parallelization
        'vectorization': vectorization,
        'unroll_factor': unroll_factor,
        'loop_interchange': loop_interchange,
        
        # Fusion and memory optimization
        'fusion_strategy': fusion_strategy,
        'use_shared_memory': use_shared_memory,
        
        # Performance tuning
        'optimize_for_latency': optimize_for_latency,
        'enable_blocking': enable_blocking,
        'enable_recomputation': enable_recomputation,
        
        # Metadata for analysis
        'optimization_strategy': 'alphaevolve_inspired',
        'target_speedup': 1.32,
    }
    
    return optimization_params

if __name__ == "__main__":
    # Test the function
    params = optimize_attention()
    print(json.dumps(params, indent=2))