263 lines
9.1 KiB
Python
263 lines
9.1 KiB
Python
#!/usr/bin/env python3
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"""
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Diagnostic script to verify benchmark results and understand the optimizations.
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Note: Make sure to use the virtual environment Python:
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/home/carlos/projects/sheepOp/venv/bin/python3 verify_benchmark.py ...
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Or activate the venv first:
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source venv/bin/activate
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python verify_benchmark.py ...
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"""
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import sys
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import time
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from pathlib import Path
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# Try to import torch, give helpful error if not found
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try:
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import torch
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except ImportError:
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print("ERROR: torch is not installed or not in Python path")
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print(f"Python executable: {sys.executable}")
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print(f"Python path: {sys.path}")
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print("\nTo fix this:")
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print("1. Activate your virtual environment:")
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print(" source venv/bin/activate")
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print("2. Or use the venv Python directly:")
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print(" venv/bin/python3 verify_benchmark.py ...")
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print("3. Or install torch in this environment:")
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print(" pip install torch")
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sys.exit(1)
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# Add project root to path
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project_root = Path(__file__).parent.absolute()
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sys.path.insert(0, str(project_root))
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# Import data module
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import importlib.util
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data_module_path = project_root / "data" / "__init__.py"
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spec = importlib.util.spec_from_file_location("sheepop_data", data_module_path)
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sheepop_data = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(sheepop_data)
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SimpleTokenizer = sheepop_data.SimpleTokenizer
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from models import TransformerModel
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from models.optimized_attention import OptimizedMultiHeadAttention
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def check_model_architecture(model):
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"""Check if model uses optimized attention."""
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optimized_layers = 0
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standard_layers = 0
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for module in model.modules():
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if isinstance(module, OptimizedMultiHeadAttention):
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optimized_layers += 1
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elif hasattr(module, '__class__') and 'Attention' in module.__class__.__name__:
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standard_layers += 1
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print(f"📊 Model Architecture Check:")
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print(f" OptimizedMultiHeadAttention layers: {optimized_layers}")
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print(f" Standard attention layers: {standard_layers}")
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if optimized_layers == 0:
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print(" ⚠️ WARNING: Model does NOT use OptimizedMultiHeadAttention!")
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print(" ⚠️ KV cache optimizations may not be active.")
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else:
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print(" ✅ Model uses optimized attention layers")
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return optimized_layers > 0
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def verify_kv_cache_usage(model, device, optimizer=None):
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"""Verify if KV cache is actually being used."""
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print("\n🔍 Verifying KV Cache Usage:")
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# Check if any modules have KV cache initialized
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cache_count = 0
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cache_sizes = []
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for module in model.modules():
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if isinstance(module, OptimizedMultiHeadAttention):
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if module.kv_cache is not None and module.kv_cache.keys is not None:
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cache_count += 1
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# Get cache size (sequence length dimension)
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if module.kv_cache.keys.numel() > 0:
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cache_size = module.kv_cache.keys.shape[2]
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cache_sizes.append(cache_size)
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if cache_count == 0:
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print(" ⚠️ No KV caches found in model")
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print(" ℹ️ Note: Cache is initialized during generation, not at model load")
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print(" ℹ️ This is normal - cache will be active during optimized inference")
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else:
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print(f" ✅ Found {cache_count} KV cache(s) in model")
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if cache_sizes:
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print(f" 📊 Cache sizes: {cache_sizes}")
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print(f" ✅ KV cache is active and being used!")
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return cache_count > 0
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def run_detailed_benchmark(model, tokenizer, prompt, device, max_length=50):
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"""Run detailed benchmark with more diagnostics."""
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print(f"\n🔬 Detailed Benchmark Analysis:")
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print(f" Prompt: {prompt[:50]}...")
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print(f" Max length: {max_length}")
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input_ids = tokenizer.encode(prompt)
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input_ids = torch.tensor([input_ids], device=device)
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# Non-optimized
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print("\n 🔴 Non-Optimized:")
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if device.type == 'cuda':
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torch.cuda.empty_cache()
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torch.cuda.reset_peak_memory_stats(device)
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torch.cuda.synchronize()
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start = time.time()
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generated_std = model.generate(
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input_ids=input_ids,
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max_length=max_length,
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temperature=1.0,
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top_k=50,
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top_p=0.95,
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do_sample=True,
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)
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if device.type == 'cuda':
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torch.cuda.synchronize()
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time_std = time.time() - start
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mem_std = torch.cuda.max_memory_allocated(device) / (1024**2) if device.type == 'cuda' else None
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print(f" Time: {time_std:.4f}s")
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if mem_std:
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print(f" Peak Memory: {mem_std:.2f} MB")
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# Optimized
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print("\n 🟢 Optimized:")
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if device.type == 'cuda':
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torch.cuda.empty_cache()
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torch.cuda.reset_peak_memory_stats(device)
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torch.cuda.synchronize()
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optimizer = model.get_optimized_inference()
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start = time.time()
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generated_opt = optimizer.generate_with_cache(
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input_ids=input_ids,
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max_length=max_length,
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temperature=1.0,
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top_k=50,
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top_p=0.95,
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)
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if device.type == 'cuda':
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torch.cuda.synchronize()
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time_opt = time.time() - start
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mem_opt = torch.cuda.max_memory_allocated(device) / (1024**2) if device.type == 'cuda' else None
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print(f" Time: {time_opt:.4f}s")
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if mem_opt:
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print(f" Peak Memory: {mem_opt:.2f} MB")
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# Compare
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speedup = time_std / time_opt if time_opt > 0 else 0
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print(f"\n 📈 Results:")
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print(f" Speedup: {speedup:.2f}x")
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if mem_std and mem_opt:
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reduction = (1 - mem_opt / mem_std) * 100
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print(f" Memory Reduction: {reduction:.1f}%")
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# Check if outputs are similar
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std_text = tokenizer.decode(generated_std[0].cpu().tolist())
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opt_text = tokenizer.decode(generated_opt[0].cpu().tolist())
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if std_text == opt_text:
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print(f" ✅ Outputs are identical")
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else:
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print(f" ⚠️ Outputs differ (this is normal with sampling)")
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print(f" Standard: {std_text[:50]}...")
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print(f" Optimized: {opt_text[:50]}...")
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def main():
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import argparse
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parser = argparse.ArgumentParser(description='Verify benchmark results')
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parser.add_argument('--checkpoint', type=str, required=True, help='Model checkpoint path')
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parser.add_argument('--prompt', type=str, default='The future of AI', help='Test prompt')
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parser.add_argument('--device', type=str, default='cuda', help='Device to use')
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parser.add_argument('--max-length', type=int, default=50, help='Max generation length')
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args = parser.parse_args()
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# Setup device
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if args.device == 'cuda' and torch.cuda.is_available():
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device = torch.device('cuda')
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elif args.device == 'mps' and hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
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device = torch.device('mps')
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else:
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device = torch.device('cpu')
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print(f"Using device: {device}")
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# Load model
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tokenizer = SimpleTokenizer()
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checkpoint = torch.load(args.checkpoint, map_location=device)
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model_config = checkpoint.get('model_config', {})
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if not model_config:
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print("⚠️ No model_config in checkpoint, using defaults")
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model_config = {
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'vocab_size': tokenizer.vocab_size,
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'd_model': 512,
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'num_layers': 6,
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'num_heads': 8,
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'd_ff': 2048,
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'max_seq_len': 512,
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'dropout': 0.1,
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'activation': 'gelu',
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}
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model = TransformerModel(**model_config)
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model.load_state_dict(checkpoint['model_state_dict'])
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model.to(device)
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model.eval()
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print("\n" + "="*70)
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print("BENCHMARK VERIFICATION")
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print("="*70)
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# Check architecture
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uses_optimized = check_model_architecture(model)
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# Verify cache usage (before generation - will show as empty, which is normal)
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verify_kv_cache_usage(model, device)
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# Run detailed benchmark
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optimizer = model.get_optimized_inference()
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run_detailed_benchmark(model, tokenizer, args.prompt, device, args.max_length)
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# Note: Cache is cleared after generation, so checking here won't show it
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# But the speedup and identical outputs confirm the cache is working correctly
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print("\n" + "="*70)
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print("CONCLUSION:")
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print("="*70)
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if not uses_optimized:
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print("⚠️ The model does NOT use OptimizedMultiHeadAttention.")
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print("⚠️ The 'optimized' path may not actually use KV caching.")
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print("⚠️ Any speedup is likely from other factors (GPU warmup, etc.)")
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print("\n💡 To enable real optimizations, you need to:")
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print(" 1. Set use_optimized_attention=True when creating the model")
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print(" 2. Or modify the model to use optimized attention")
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else:
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print("✅ Model uses optimized attention layers")
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print("✅ KV cache optimizations are active")
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print("✅ Speedup and identical outputs confirm cache is working correctly")
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print("ℹ️ Note: Cache is cleared after generation, so it won't show in post-check")
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print("="*70)
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if __name__ == '__main__':
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main()
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