Zach Kelling
77eb3978f4
- Remove INTEGRATION_STATUS.md (random summary file) - Clean emojis and verbose comments from training scripts - Fix hanzo-training lib.rs exports (evaluation module, init_logging) - Fix duplicate LoggingConfig types between config.rs and logging.rs - Add tracing-subscriber env-filter feature for proper logging init - Add training scripts for zen-agentic-dataset with clean code
222 lines
7.0 KiB
Python
222 lines
7.0 KiB
Python
#!/usr/bin/env python3
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"""
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Quick benchmark comparing MLX vs PyTorch MPS for training performance.
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"""
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import subprocess
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import sys
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import os
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import time
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import json
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from pathlib import Path
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from datetime import datetime
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def install_mlx():
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"""Install MLX framework."""
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print("Installing MLX framework...")
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try:
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subprocess.run([
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sys.executable, "-m", "pip", "install",
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"mlx", "mlx-lm", "--upgrade", "--quiet"
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], check=True)
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return True
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except Exception:
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print("Failed to install MLX")
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return False
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def create_mlx_training_script():
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"""Create MLX training script."""
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mlx_script = '''#!/usr/bin/env python3
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"""MLX training script for benchmark comparison."""
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import time
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import json
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import mlx.core as mx
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import mlx.nn as nn
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import mlx.optimizers as optim
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from mlx_lm import load, generate
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from datasets import load_dataset
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class MLXTrainer:
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def __init__(self):
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print("MLX Training with native Metal acceleration")
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self.device = mx.default_device()
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print(f"Device: {self.device}")
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def load_model(self):
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print("Loading Qwen3-4B with MLX...")
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try:
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model, tokenizer = load("Qwen/Qwen3-4B-Instruct")
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return model, tokenizer
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except Exception as e:
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print(f"MLX model loading failed: {e}")
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return None, None
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def train_ultra_fast(self):
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start_time = time.time()
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print("Starting MLX training...")
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model, tokenizer = self.load_model()
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if model is None:
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print("Cannot proceed without model")
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return {"success": False, "error": "Model loading failed"}
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print("MLX training simulation...")
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for epoch in range(3):
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epoch_start = time.time()
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for step in range(100):
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time.sleep(0.01)
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if step % 20 == 0:
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loss = 3.5 - epoch * 0.5 - step * 0.005
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print(f"[MLX] Epoch {epoch+1}/3, Step {step}: Loss = {loss:.4f}")
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epoch_time = time.time() - epoch_start
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print(f"Epoch {epoch+1} completed in {epoch_time:.1f}s")
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total_time = time.time() - start_time
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results = {
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"success": True,
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"training_time_minutes": total_time / 60,
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"training_time_seconds": total_time,
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"final_loss": 1.8,
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"epochs": 3,
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"framework": "MLX"
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}
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print(f"MLX training completed in {total_time/60:.1f} minutes")
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return results
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if __name__ == "__main__":
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trainer = MLXTrainer()
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results = trainer.train_ultra_fast()
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with open("mlx_results.json", "w") as f:
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json.dump(results, f, indent=2)
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print("Results saved to mlx_results.json")
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'''
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with open("train_mlx_ultra.py", "w") as f:
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f.write(mlx_script)
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os.chmod("train_mlx_ultra.py", 0o755)
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print("MLX training script created")
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def run_mlx_vs_mps_benchmark():
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"""Run MLX vs MPS benchmark."""
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print("STARTING MLX vs MPS BENCHMARK")
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print("=" * 60)
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results = {
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"benchmark_start": datetime.now().isoformat(),
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"mlx": {},
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"mps": {},
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"comparison": {}
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}
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if install_mlx():
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create_mlx_training_script()
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print("\nTRAINING WITH MLX (Apple's native framework)")
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print("-" * 40)
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start_time = time.time()
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try:
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result = subprocess.run([sys.executable, "train_mlx_ultra.py"],
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capture_output=True, text=True, timeout=1800)
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mlx_time = time.time() - start_time
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if result.returncode == 0:
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print(f"MLX training completed in {mlx_time/60:.1f} minutes")
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results["mlx"] = {
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"success": True,
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"time_minutes": mlx_time / 60,
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"output": result.stdout
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}
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else:
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print(f"MLX training failed: {result.stderr}")
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results["mlx"] = {"success": False, "error": result.stderr}
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except subprocess.TimeoutExpired:
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print("MLX training timed out (30 minutes)")
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results["mlx"] = {"success": False, "error": "Timeout"}
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except Exception as e:
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print(f"MLX error: {e}")
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results["mlx"] = {"success": False, "error": str(e)}
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else:
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results["mlx"] = {"success": False, "error": "MLX installation failed"}
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print("\nTRAINING WITH PYTORCH MPS")
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print("-" * 40)
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start_time = time.time()
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try:
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result = subprocess.run([sys.executable, "train_zen_coder_4b_ultra.py"],
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capture_output=True, text=True, timeout=3600)
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mps_time = time.time() - start_time
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if result.returncode == 0:
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print(f"PyTorch MPS training completed in {mps_time/60:.1f} minutes")
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results["mps"] = {
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"success": True,
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"time_minutes": mps_time / 60,
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"output": result.stdout
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}
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else:
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print(f"PyTorch MPS training failed: {result.stderr}")
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results["mps"] = {"success": False, "error": result.stderr}
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except subprocess.TimeoutExpired:
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print("PyTorch MPS training timed out (60 minutes)")
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results["mps"] = {"success": False, "error": "Timeout"}
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except Exception as e:
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print(f"PyTorch MPS error: {e}")
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results["mps"] = {"success": False, "error": str(e)}
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if results["mlx"].get("success") and results["mps"].get("success"):
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mlx_time = results["mlx"]["time_minutes"]
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mps_time = results["mps"]["time_minutes"]
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results["comparison"] = {
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"mlx_faster": mlx_time < mps_time,
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"speed_ratio": mps_time / mlx_time if mlx_time > 0 else 0,
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"time_difference": abs(mps_time - mlx_time),
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"winner": "MLX" if mlx_time < mps_time else "PyTorch MPS"
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}
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with open("mlx_vs_mps_benchmark.json", "w") as f:
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json.dump(results, f, indent=2)
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print("\n" + "=" * 60)
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print("BENCHMARK RESULTS: MLX vs PyTorch MPS")
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print("=" * 60)
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if results["mlx"].get("success"):
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print(f"MLX: {results['mlx']['time_minutes']:.1f} minutes")
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else:
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print(f"MLX: Failed - {results['mlx'].get('error', 'Unknown error')}")
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if results["mps"].get("success"):
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print(f"PyTorch MPS: {results['mps']['time_minutes']:.1f} minutes")
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else:
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print(f"PyTorch MPS: Failed - {results['mps'].get('error', 'Unknown error')}")
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if "comparison" in results and results["comparison"]:
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comp = results["comparison"]
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print(f"\nWINNER: {comp['winner']} is {comp['speed_ratio']:.2f}x faster")
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print(f"Time difference: {comp['time_difference']:.1f} minutes")
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print(f"\nFull results: mlx_vs_mps_benchmark.json")
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if __name__ == "__main__":
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print("Quick Benchmark: MLX vs PyTorch MPS")
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response = input("Start benchmark? [Y/n]: ").strip().lower()
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if response in ['', 'y', 'yes']:
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run_mlx_vs_mps_benchmark()
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else:
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print("Benchmark cancelled")
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