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#!/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)) |