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#!/usr/bin/env python3
"""
Simple Test Script for Optimized MLX Attention
This script demonstrates how to monkey patch the official mlx-lm library
with the AlphaEvolve optimized attention kernel and shows the performance
difference on a test prompt.
Usage:
python test_optimized_attention.py [path_to_best_program.py]
If no path is provided, it will use the default best_program.py from
openevolve_output/best/
"""
import os
import sys
import time
import argparse
import subprocess
import tempfile
from typing import Optional, Dict, Any
import traceback
def find_best_program() -> Optional[str]:
"""Find the best_program.py file in the expected location"""
# Default location
default_path = os.path.join(
os.path.dirname(__file__), "openevolve_output", "best", "best_program.py"
)
if os.path.exists(default_path):
return default_path
# Alternative locations to check
alternatives = [
"best_program.py",
"openevolve_output/best/best_program.py",
"../best_program.py",
]
for alt in alternatives:
if os.path.exists(alt):
return alt
return None
def load_custom_attention_class(program_path: str):
"""Load the CustomGQAAttention class from the evolved program"""
print(f"📁 Loading optimized attention from: {program_path}")
try:
# Read the program
with open(program_path, "r") as f:
program_text = f.read()
# Setup execution environment
import mlx.core as mx
import mlx.nn as nn
import numpy as np
from typing import Optional, Tuple, Any
exec_globals = {
"__builtins__": __builtins__,
"mx": mx,
"nn": nn,
"np": np,
"time": time,
"Optional": Optional,
"Tuple": Tuple,
"Any": Any,
}
# Add mlx_lm imports for RoPE
try:
exec_globals["mlx_lm"] = __import__("mlx_lm")
except ImportError:
print("⚠️ Could not import mlx_lm, RoPE may not work")
# Execute the program
exec(program_text, exec_globals)
# Extract the custom attention class
custom_class = exec_globals.get("CustomGQAAttention")
if custom_class is None:
raise ValueError("CustomGQAAttention class not found in program")
print("✅ Successfully loaded CustomGQAAttention class")
return custom_class
except Exception as e:
print(f"❌ Failed to load custom attention: {e}")
traceback.print_exc()
return None
def apply_monkey_patch(custom_attention_class):
"""Apply monkey patch to replace Qwen3 attention with custom implementation"""
print("🔧 Applying monkey patch to mlx-lm...")
try:
import mlx_lm.models.qwen3 as qwen3_module
# Store original attention class
original_attention = qwen3_module.Attention
# Replace with custom implementation
qwen3_module.Attention = custom_attention_class
print("✅ Successfully applied monkey patch")
return original_attention
except ImportError as e:
print(f"❌ Could not import mlx_lm.models.qwen3: {e}")
print(" Make sure mlx-lm is installed: pip install mlx-lm")
return None
except Exception as e:
print(f"❌ Failed to apply monkey patch: {e}")
return None
def remove_monkey_patch(original_attention):
"""Remove the monkey patch and restore original attention"""
if original_attention is None:
return
try:
import mlx_lm.models.qwen3 as qwen3_module
qwen3_module.Attention = original_attention
print("✅ Removed monkey patch")
except ImportError:
pass
def run_mlx_lm_generation(
prompt: str,
max_tokens: int = 1000,
model: str = "mlx-community/Qwen3-0.6B-bf16",
debug: bool = False,
) -> Dict[str, Any]:
"""Run mlx-lm generation and parse the output"""
print(f"🧪 Running generation with prompt: '{prompt[:50]}...'")
try:
# Also need to update the deprecated command format
cmd = [
"python",
"-m",
"mlx_lm",
"generate", # Updated format
"--model",
model,
"--prompt",
prompt,
"--max-tokens",
str(max_tokens),
"--temp",
"0.1", # Low temperature for consistent results
]
if debug:
print(f"🔧 Running command: {' '.join(cmd)}")
# Run generation
start_time = time.perf_counter()
result = subprocess.run(cmd, capture_output=True, text=True, timeout=120)
end_time = time.perf_counter()
if debug:
print(f"📤 Command output:")
print(f"Return code: {result.returncode}")
print(f"STDOUT length: {len(result.stdout)}")
print(f"STDERR length: {len(result.stderr)}")
if result.stdout:
print("First 500 chars of stdout:")
print(result.stdout[:500])
if result.stderr:
print("STDERR:")
print(result.stderr[:500])
if result.returncode != 0:
print(f"❌ Generation failed with return code {result.returncode}")
if result.stderr:
print(f"Error: {result.stderr[:200]}")
return {"success": False, "error": result.stderr}
# Parse output
output_lines = result.stdout.strip().split("\n")
prompt_tokens = 0
generation_tokens = 0
prompt_speed = 0.0
generation_speed = 0.0
peak_memory = 0.0
generated_text = ""
# Find the generated text (everything after the prompt)
capture_text = False
found_prompt_stats = False
found_generation_stats = False
for line in output_lines:
if debug:
print(f"Parsing line: {line[:100]}")
if line.startswith("=========="):
capture_text = True
continue
elif (
capture_text
and line.strip()
and not line.startswith("Prompt:")
and not line.startswith("Generation:")
and not line.startswith("Peak memory:")
):
generated_text += line + "\n"
elif "Prompt:" in line and "tokens-per-sec" in line:
try:
# Parse: "Prompt: 9 tokens, 245.085 tokens-per-sec"
parts = line.split(",")
prompt_tokens = int(parts[0].split(":")[1].strip().split()[0])
prompt_speed = float(parts[1].strip().split()[0])
found_prompt_stats = True
if debug:
print(f"Found prompt stats: {prompt_tokens} tokens, {prompt_speed} tok/sec")
except (ValueError, IndexError) as e:
if debug:
print(f"Failed to parse prompt line: {e}")
elif "Generation:" in line and "tokens-per-sec" in line:
try:
# Parse: "Generation: 82 tokens, 77.143 tokens-per-sec"
parts = line.split(",")
generation_tokens = int(parts[0].split(":")[1].strip().split()[0])
generation_speed = float(parts[1].strip().split()[0])
found_generation_stats = True
if debug:
print(
f"Found generation stats: {generation_tokens} tokens, {generation_speed} tok/sec"
)
except (ValueError, IndexError) as e:
if debug:
print(f"Failed to parse generation line: {e}")
elif "Peak memory:" in line:
try:
memory_str = line.split(":")[1].strip()
if "GB" in memory_str:
peak_memory = float(memory_str.replace("GB", "").strip())
elif "MB" in memory_str:
peak_memory = float(memory_str.replace("MB", "").strip()) / 1024
if debug:
print(f"Found memory: {peak_memory} GB")
except (ValueError, IndexError) as e:
if debug:
print(f"Failed to parse memory line: {e}")
# Check if we got meaningful results
if not found_generation_stats or generation_tokens == 0:
print("⚠️ No generation statistics found in output")
if debug:
print(f"found_prompt_stats: {found_prompt_stats}")
print(f"found_generation_stats: {found_generation_stats}")
print(f"generation_tokens: {generation_tokens}")
print("Full output for debugging:")
print(result.stdout)
return {"success": False, "error": "No generation statistics found"}
result_dict = {
"success": True,
"prompt_tokens": prompt_tokens,
"generation_tokens": generation_tokens,
"prompt_speed": prompt_speed,
"generation_speed": generation_speed,
"peak_memory": peak_memory,
"total_time": end_time - start_time,
"generated_text": generated_text.strip(),
"full_output": result.stdout,
}
if debug:
print(f"Parsed result: {result_dict}")
return result_dict
except subprocess.TimeoutExpired:
print("⏰ Generation timed out after 120 seconds")
return {"success": False, "error": "Timeout"}
except Exception as e:
print(f"❌ Generation failed: {e}")
if debug:
traceback.print_exc()
return {"success": False, "error": str(e)}
def run_comparison_test(
prompt: str, custom_attention_class, max_tokens: int = 1000, debug: bool = False
):
"""Run comparison test between standard and optimized attention"""
print(f"\n{'='*60}")
print("🔬 ATTENTION COMPARISON TEST")
print(f"{'='*60}")
print(f"Prompt: {prompt}")
print(f"Max tokens: {max_tokens}")
print()
# Test 1: Standard attention
print("📊 Testing STANDARD attention...")
standard_result = run_mlx_lm_generation(prompt, max_tokens, debug=debug)
if not standard_result.get("success", False):
print("❌ Standard attention test failed")
if debug and "error" in standard_result:
print(f" Error: {standard_result['error']}")
print("\n🔧 Troubleshooting tips:")
print(" • Check that mlx-lm is installed: pip install mlx-lm")
print(" • Try a shorter prompt or fewer tokens")
print(" • Run with --debug flag for more info")
print(" • Check if the model downloads successfully")
return
print(f"✅ Standard Results:")
print(f" Decode Speed: {standard_result['generation_speed']:.1f} tokens/sec")
print(f" Memory Usage: {standard_result['peak_memory']:.2f} GB")
print(f" Total Time: {standard_result['total_time']:.2f} seconds")
print(f" Generated: {standard_result['generation_tokens']} tokens")
# Check if we have valid results
if standard_result["generation_tokens"] == 0:
print("⚠️ Warning: Standard attention generated 0 tokens")
print(" This might indicate an issue with the model or prompt")
print(" Generated text preview:")
print(f" '{standard_result['generated_text'][:100]}'")
# Ask user if they want to continue
try:
response = input("\n❓ Continue with optimized test anyway? (y/n): ").lower()
if response != "y":
print("Test cancelled")
return
except KeyboardInterrupt:
print("\nTest cancelled")
return
# Apply monkey patch
original_attention = apply_monkey_patch(custom_attention_class)
if original_attention is None:
print("❌ Failed to apply monkey patch")
return
try:
# Test 2: Optimized attention
print("\n📊 Testing OPTIMIZED attention...")
optimized_result = run_mlx_lm_generation(prompt, max_tokens, debug=debug)
if not optimized_result.get("success", False):
print("❌ Optimized attention test failed")
if debug and "error" in optimized_result:
print(f" Error: {optimized_result['error']}")
return
print(f"✅ Optimized Results:")
print(f" Decode Speed: {optimized_result['generation_speed']:.1f} tokens/sec")
print(f" Memory Usage: {optimized_result['peak_memory']:.2f} GB")
print(f" Total Time: {optimized_result['total_time']:.2f} seconds")
print(f" Generated: {optimized_result['generation_tokens']} tokens")
# Calculate improvements (handle division by zero)
if standard_result["generation_speed"] > 0:
speed_improvement = (
(optimized_result["generation_speed"] - standard_result["generation_speed"])
/ standard_result["generation_speed"]
) * 100
else:
speed_improvement = 0.0
print("⚠️ Cannot calculate speed improvement (standard speed was 0)")
memory_change = optimized_result["peak_memory"] - standard_result["peak_memory"]
if standard_result["total_time"] > 0:
time_improvement = (
(standard_result["total_time"] - optimized_result["total_time"])
/ standard_result["total_time"]
) * 100
else:
time_improvement = 0.0
print(f"\n🚀 PERFORMANCE COMPARISON:")
if standard_result["generation_speed"] > 0:
print(f" Speed Improvement: {speed_improvement:+.1f}%")
else:
print(
f" Speed Comparison: {standard_result['generation_speed']:.1f} → {optimized_result['generation_speed']:.1f} tokens/sec"
)
print(f" Memory Change: {memory_change:+.2f} GB")
print(f" Time Improvement: {time_improvement:+.1f}%")
if speed_improvement > 5:
print("🎯 SIGNIFICANT IMPROVEMENT achieved!")
elif speed_improvement > 0:
print("📈 Modest improvement achieved")
elif standard_result["generation_speed"] == 0 and optimized_result["generation_speed"] > 0:
print("🔥 Optimized version works where standard failed!")
else:
print("⚠️ No improvement or regression")
# Show generated text comparison
print(f"\n📝 GENERATED TEXT COMPARISON:")
std_text = (
standard_result["generated_text"][:200]
if standard_result["generated_text"]
else "[No text generated]"
)
opt_text = (
optimized_result["generated_text"][:200]
if optimized_result["generated_text"]
else "[No text generated]"
)
print(f"Standard: {std_text}...")
print(f"Optimized: {opt_text}...")
if standard_result["generated_text"] and optimized_result["generated_text"]:
if standard_result["generated_text"][:100] == optimized_result["generated_text"][:100]:
print("✅ Generated text is identical (good!)")
else:
print("⚠️ Generated text differs (check randomness/temperature)")
elif not standard_result["generated_text"] and not optimized_result["generated_text"]:
print("⚠️ Both versions generated no text")
else:
print("ℹ️ Different text generation behavior")
finally:
# Always remove monkey patch
remove_monkey_patch(original_attention)
def main():
parser = argparse.ArgumentParser(description="Test optimized MLX attention kernel")
parser.add_argument("program_path", nargs="?", help="Path to best_program.py")
parser.add_argument(
"--prompt", default="The future of artificial intelligence is", help="Test prompt"
)
parser.add_argument("--max-tokens", type=int, default=100, help="Maximum tokens to generate")
parser.add_argument("--model", default="mlx-community/Qwen3-0.6B-bf16", help="Model to use")
parser.add_argument("--debug", action="store_true", help="Enable debug output")
args = parser.parse_args()
# Find program path
if args.program_path:
program_path = args.program_path
else:
program_path = find_best_program()
if not program_path or not os.path.exists(program_path):
print("❌ Could not find best_program.py")
print(" Please provide the path to the optimized program:")
print(" python test_optimized_attention.py path/to/best_program.py")
print("\n Or make sure you have run AlphaEvolve and have results in:")
print(" openevolve_output/best/best_program.py")
sys.exit(1)
print("🚀 MLX Optimized Attention Tester")
print(f"Using program: {program_path}")
print(f"Model: {args.model}")
if args.debug:
print("🐛 Debug mode enabled")
# Load custom attention
custom_attention_class = load_custom_attention_class(program_path)
if custom_attention_class is None:
sys.exit(1)
# Check if mlx-lm is available
try:
import mlx_lm
print("✅ mlx-lm is available")
except ImportError:
print("❌ mlx-lm is not installed")
print(" Please install it: pip install mlx-lm")
sys.exit(1)
# Run comparison test
run_comparison_test(args.prompt, custom_attention_class, args.max_tokens, debug=args.debug)
print(f"\n{'='*60}")
print("✅ Test completed!")
print("💡 To test with a different prompt:")
print(f" python {sys.argv[0]} --prompt 'Your custom prompt here'")
print("💡 For debugging: add --debug flag")
print("💡 For help: python test_optimized_attention.py --help")
if __name__ == "__main__":
main()
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