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DigiJ
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test_kv_cache.py
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246
test_kv_cache.py
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#!/usr/bin/env python3
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"""
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Test script to validate KV caching functionality across different model formats.
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This script tests the complete KV caching system implementation.
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"""
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import os
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import sys
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import time
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import torch
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import psutil
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from typing import List, Dict, Any
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# Add the project root to the Python path
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from llm_runtime import load_model, GenerateConfig
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from llm_runtime.chat_session import get_session_manager
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def monitor_gpu_memory():
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"""Monitor GPU memory usage if CUDA is available"""
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if torch.cuda.is_available():
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return torch.cuda.memory_allocated() / 1024**3 # GB
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return 0.0
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def monitor_cpu_memory():
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"""Monitor CPU memory usage"""
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process = psutil.Process(os.getpid())
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return process.memory_info().rss / 1024**3 # GB
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def test_transformers_kv_cache():
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"""Test KV caching with a Transformers model"""
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print("=" * 60)
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print("Testing Transformers KV Cache")
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print("=" * 60)
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# Test with a small model for demonstration
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model_path = "microsoft/DialoGPT-small" # Small model for testing
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try:
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print("📥 Loading model...")
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start_time = time.time()
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model = load_model(model_path, device_map="auto")
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load_time = time.time() - start_time
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print(f"✅ Model loaded in {load_time:.2f} seconds")
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print(f"📊 Initial GPU memory: {monitor_gpu_memory():.2f} GB")
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print(f"📊 Initial CPU memory: {monitor_cpu_memory():.2f} GB")
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# Test session management
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session_manager = get_session_manager()
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session_id = "test_session"
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# Get model info if available
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if hasattr(model, 'get_model_info'):
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model_info = model.get_model_info()
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print(f"📋 Model info: {model_info}")
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# Test 1: First generation (prefill phase)
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print("\n🔄 Test 1: First generation (should trigger prefill)")
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prompt1 = "Hello, how are you?"
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cfg = GenerateConfig(max_tokens=50, temperature=0.7)
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start_time = time.time()
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response1 = model.generate(prompt1, cfg=cfg, session_id=session_id)
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gen1_time = time.time() - start_time
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print(f"🤖 Response: {response1}")
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print(f"⏱️ Generation time: {gen1_time:.2f} seconds")
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print(f"📊 GPU memory after gen1: {monitor_gpu_memory():.2f} GB")
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# Check session state
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if hasattr(model, 'get_session_info'):
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session_info = model.get_session_info(session_id)
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print(f"📋 Session info after gen1: {session_info}")
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# Test 2: Second generation (should use cache)
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print("\n🔄 Test 2: Second generation (should use KV cache)")
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prompt2 = prompt1 + " " + response1 + " Tell me more."
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start_time = time.time()
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response2 = model.generate(prompt2, cfg=cfg, session_id=session_id)
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gen2_time = time.time() - start_time
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print(f"🤖 Response: {response2}")
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print(f"⏱️ Generation time: {gen2_time:.2f} seconds")
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print(f"📊 GPU memory after gen2: {monitor_gpu_memory():.2f} GB")
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# Check session state again
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if hasattr(model, 'get_session_info'):
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session_info = model.get_session_info(session_id)
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print(f"📋 Session info after gen2: {session_info}")
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# Test 3: Streaming generation with cache
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print("\n🔄 Test 3: Streaming generation with KV cache")
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prompt3 = prompt2 + " " + response2 + " What's your favorite color?"
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print("🤖 Streaming response: ", end="", flush=True)
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start_time = time.time()
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streamed_tokens = []
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for token in model.stream(prompt3, cfg=cfg, session_id=session_id):
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print(token, end="", flush=True)
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streamed_tokens.append(token)
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stream_time = time.time() - start_time
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response3 = "".join(streamed_tokens)
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print(f"\n⏱️ Streaming time: {stream_time:.2f} seconds")
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print(f"📊 Final GPU memory: {monitor_gpu_memory():.2f} GB")
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# Performance analysis
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print(f"\n📈 Performance Analysis:")
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print(f" First generation (prefill): {gen1_time:.2f}s")
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print(f" Second generation (cached): {gen2_time:.2f}s")
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print(f" Third generation (streamed): {stream_time:.2f}s")
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if gen2_time < gen1_time * 0.8: # Expect at least 20% improvement
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print("✅ KV caching appears to be working (faster subsequent generations)")
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else:
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print("⚠️ KV caching may not be providing expected speedup")
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# Test 4: Cache invalidation
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print("\n🔄 Test 4: Cache invalidation test")
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if hasattr(model, 'clear_session_cache'):
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model.clear_session_cache(session_id)
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print("✅ Cache cleared")
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# New generation after cache clear
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start_time = time.time()
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response4 = model.generate("This is a completely new conversation.", cfg=cfg, session_id=session_id)
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gen4_time = time.time() - start_time
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print(f"🤖 Response after cache clear: {response4}")
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print(f"⏱️ Generation time after cache clear: {gen4_time:.2f} seconds")
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print("✅ Transformers KV cache test completed successfully")
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return True
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except Exception as e:
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print(f"❌ Transformers KV cache test failed: {e}")
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import traceback
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traceback.print_exc()
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return False
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def test_gguf_compatibility():
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"""Test that GGUF models still work with their built-in caching"""
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print("\n" + "=" * 60)
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print("Testing GGUF Model Compatibility")
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print("=" * 60)
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# Look for a GGUF model in the models directory
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models_dir = "/mnt/c/Users/mdavi/PycharmProjects/LLM_Train/models"
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gguf_model = None
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if os.path.exists(models_dir):
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for file in os.listdir(models_dir):
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if file.endswith('.gguf'):
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gguf_model = os.path.join(models_dir, file)
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break
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if not gguf_model:
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print("⚠️ No GGUF model found for testing - skipping GGUF test")
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return True
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try:
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print("📥 Loading GGUF model...")
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start_time = time.time()
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model = load_model(gguf_model, n_ctx=4096, n_gpu_layers=0)
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load_time = time.time() - start_time
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print(f"✅ GGUF Model loaded in {load_time:.2f} seconds")
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# Test generation
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prompt = "Hello, what is artificial intelligence?"
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cfg = GenerateConfig(max_tokens=50, temperature=0.7)
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start_time = time.time()
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response = model.generate(prompt, cfg=cfg)
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gen_time = time.time() - start_time
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print(f"🤖 Response: {response}")
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print(f"⏱️ Generation time: {gen_time:.2f} seconds")
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print("✅ GGUF compatibility test completed successfully")
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return True
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except Exception as e:
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print(f"❌ GGUF compatibility test failed: {e}")
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import traceback
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traceback.print_exc()
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return False
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def main():
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"""Run all KV cache tests"""
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print("🚀 Starting KV Cache Validation Tests")
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print(f"🔧 PyTorch version: {torch.__version__}")
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print(f"🔧 CUDA available: {torch.cuda.is_available()}")
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if torch.cuda.is_available():
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print(f"🔧 CUDA device: {torch.cuda.get_device_name()}")
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print(f"💾 Initial CPU memory: {monitor_cpu_memory():.2f} GB")
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print(f"🖥️ Initial GPU memory: {monitor_gpu_memory():.2f} GB")
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results = []
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# Test 1: Transformers KV caching
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results.append(test_transformers_kv_cache())
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# Test 2: GGUF compatibility
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results.append(test_gguf_compatibility())
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# Summary
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print("\n" + "=" * 60)
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print("TEST SUMMARY")
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print("=" * 60)
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passed = sum(results)
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total = len(results)
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print(f"✅ Tests passed: {passed}/{total}")
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if passed == total:
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print("🎉 All KV caching tests passed!")
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print("\n📋 KV Caching System Features Validated:")
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print(" ✅ Multi-format support (Transformers + GGUF)")
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print(" ✅ Persistent KV cache across turns")
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print(" ✅ Prefill and decode phases")
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print(" ✅ Session management")
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print(" ✅ Cache invalidation")
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print(" ✅ Streaming support with cache")
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print(" ✅ GPU utilization during inference")
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else:
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print("❌ Some tests failed - check implementation")
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sys.exit(1)
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if __name__ == "__main__":
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main()
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