Advanced Usage Patterns and Optimization
Advanced Inference Optimization
python
import torch
import time
import psutil
from typing import Dict, List, Tuple, Optional
from dataclasses import dataclass
from transformers import AutoModelForCausalLM, AutoTokenizer
@dataclass
class InferenceMetrics:
"""Comprehensive inference metrics for BitNET"""
tokens_per_second: float
memory_usage_mb: float
energy_efficiency_score: float
latency_ms: float
throughput_requests_per_minute: float
class AdvancedBitNETOptimizer:
"""Advanced optimization strategies for BitNET deployment"""
def __init__(self, model_name: str = "microsoft/bitnet-b1.58-2B-4T"):
self.model_name = model_name
self.model = None
self.tokenizer = None
self.optimization_cache = {}
self._load_optimized_model()
def _load_optimized_model(self):
"""Load model with comprehensive optimizations"""
# Load tokenizer
self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
# Load model with optimizations
self.model = AutoModelForCausalLM.from_pretrained(
self.model_name,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True,
low_cpu_mem_usage=True,
torch_compile=True if hasattr(torch, 'compile') else False
)
# Set model to evaluation mode
self.model.eval()
# Enable optimizations
if torch.cuda.is_available():
torch.backends.cuda.enable_flash_sdp(True)
torch.backends.cuda.enable_math_sdp(True)
torch.backends.cuda.enable_mem_efficient_sdp(True)
def benchmark_inference_patterns(
self,
test_prompts: List[str],
optimization_levels: List[str] = ["baseline", "optimized", "aggressive"]
) -> Dict[str, InferenceMetrics]:
"""Benchmark different optimization patterns"""
results = {}
for opt_level in optimization_levels:
print(f"Benchmarking {opt_level} optimization...")
total_tokens = 0
total_time = 0
memory_usage = []
latencies = []
# Configure optimization level
self._apply_optimization_level(opt_level)
for prompt in test_prompts:
metrics = self._measure_single_inference(prompt)
total_tokens += metrics["tokens_generated"]
total_time += metrics["generation_time"]
memory_usage.append(metrics["memory_mb"])
latencies.append(metrics["latency_ms"])
# Calculate aggregate metrics
avg_memory = sum(memory_usage) / len(memory_usage)
avg_latency = sum(latencies) / len(latencies)
tokens_per_second = total_tokens / total_time if total_time > 0 else 0
# Estimate energy efficiency (simplified calculation)
baseline_tps = 10 # Baseline tokens per second
efficiency_score = (tokens_per_second / baseline_tps) * (400 / avg_memory) # Relative to 400MB baseline
results[opt_level] = InferenceMetrics(
tokens_per_second=tokens_per_second,
memory_usage_mb=avg_memory,
energy_efficiency_score=efficiency_score,
latency_ms=avg_latency,
throughput_requests_per_minute=60 / (avg_latency / 1000) if avg_latency > 0 else 0
)
return results
def _apply_optimization_level(self, level: str):
"""Apply specific optimization configurations"""
if level == "baseline":
# Minimal optimizations
torch.set_num_threads(1)
elif level == "optimized":
# Balanced optimizations
torch.set_num_threads(min(4, torch.get_num_threads()))
# Enable inference optimizations
if hasattr(self.model, 'config'):
self.model.config.use_cache = True
elif level == "aggressive":
# Maximum optimizations
torch.set_num_threads(min(8, torch.get_num_threads()))
# Enable all optimizations
if hasattr(self.model, 'config'):
self.model.config.use_cache = True
# Enable torch compile if available
if hasattr(torch, 'compile') and not hasattr(self.model, '_compiled'):
try:
self.model = torch.compile(self.model, mode="reduce-overhead")
self.model._compiled = True
except Exception as e:
print(f"Torch compile failed: {e}")
def _measure_single_inference(self, prompt: str) -> Dict[str, float]:
"""Measure metrics for single inference"""
# Prepare input
messages = [{"role": "user", "content": prompt}]
formatted_prompt = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = self.tokenizer(formatted_prompt, return_tensors="pt").to(self.model.device)
# Measure memory before
memory_before = psutil.Process().memory_info().rss / 1024 / 1024
# Measure inference
start_time = time.time()
with torch.no_grad():
outputs = self.model.generate(
**inputs,
max_new_tokens=100,
do_sample=True,
temperature=0.7,
early_stopping=True,
pad_token_id=self.tokenizer.eos_token_id
)
end_time = time.time()
# Measure memory after
memory_after = psutil.Process().memory_info().rss / 1024 / 1024
generation_time = end_time - start_time
tokens_generated = outputs.shape[1] - inputs['input_ids'].shape[1]
memory_used = max(0, memory_after - memory_before)
return {
"generation_time": generation_time,
"tokens_generated": tokens_generated,
"memory_mb": memory_used,
"latency_ms": generation_time * 1000
}
def optimize_for_deployment_scenario(self, scenario: str) -> Dict[str, any]:
"""Optimize BitNET for specific deployment scenarios"""
scenarios = {
"mobile": {
"max_threads": 2,
"memory_limit_mb": 512,
"priority": "latency",
"quantization": "8bit"
},
"edge": {
"max_threads": 4,
"memory_limit_mb": 1024,
"priority": "efficiency",
"quantization": "native"
},
"server": {
"max_threads": 8,
"memory_limit_mb": 2048,
"priority": "throughput",
"quantization": "native"
},
"cloud": {
"max_threads": 16,
"memory_limit_mb": 4096,
"priority": "scalability",
"quantization": "native"
}
}
if scenario not in scenarios:
raise ValueError(f"Unknown scenario: {scenario}")
config = scenarios[scenario]
# Apply scenario-specific optimizations
torch.set_num_threads(config["max_threads"])
# Configure model for scenario
optimization_settings = {
"scenario": scenario,
"configuration": config,
"estimated_memory_mb": 400, # BitNET base memory
"recommended_batch_size": self._calculate_batch_size(config["memory_limit_mb"]),
"optimization_tips": self._get_optimization_tips(scenario)
}
return optimization_settings
def _calculate_batch_size(self, memory_limit_mb: int) -> int:
"""Calculate optimal batch size for memory limit"""
base_memory = 400 # BitNET base memory
memory_per_request = 50 # Estimated memory per request
available_memory = memory_limit_mb - base_memory
if available_memory <= 0:
return 1
return max(1, available_memory // memory_per_request)
def _get_optimization_tips(self, scenario: str) -> List[str]:
"""Get scenario-specific optimization tips"""
tips = {
"mobile": [
"Use quantized models for minimal memory footprint",
"Enable early stopping for faster response",
"Consider context length limitations",
"Implement request queuing for better UX"
],
"edge": [
"Leverage CPU optimizations with bitnet.cpp",
"Implement caching for repeated requests",
"Monitor thermal throttling",
"Use efficient tokenization"
],
"server": [
"Implement batch processing for throughput",
"Use connection pooling",
"Enable request caching",
"Monitor resource utilization"
],
"cloud": [
"Use auto-scaling based on demand",
"Implement load balancing",
"Enable distributed inference",
"Monitor cost vs performance"
]
}
return tips.get(scenario, ["Optimize based on specific requirements"])
# Example advanced optimization usage
def advanced_optimization_example():
"""Demonstrate advanced BitNET optimization techniques"""
optimizer = AdvancedBitNETOptimizer()
# Test prompts for benchmarking
test_prompts = [
"Explain machine learning in simple terms",
"What are the benefits of renewable energy?",
"How does quantum computing work?",
"Describe the importance of sustainable development"
]
print("=== BitNET Advanced Optimization Benchmark ===\n")
# Benchmark different optimization levels
benchmark_results = optimizer.benchmark_inference_patterns(test_prompts)
print("Optimization Level Comparison:")
for level, metrics in benchmark_results.items():
print(f"\n{level.upper()} Optimization:")
print(f" Tokens/Second: {metrics.tokens_per_second:.1f}")
print(f" Memory Usage: {metrics.memory_usage_mb:.1f} MB")
print(f" Latency: {metrics.latency_ms:.1f} ms")
print(f" Efficiency Score: {metrics.energy_efficiency_score:.2f}")
print(f" Throughput: {metrics.throughput_requests_per_minute:.1f} req/min")
# Scenario-specific optimizations
scenarios = ["mobile", "edge", "server", "cloud"]
print("\n=== Deployment Scenario Optimizations ===\n")
for scenario in scenarios:
config = optimizer.optimize_for_deployment_scenario(scenario)
print(f"{scenario.upper()} Deployment:")
print(f" Memory Limit: {config['configuration']['memory_limit_mb']} MB")
print(f" Recommended Batch Size: {config['recommended_batch_size']}")
print(f" Priority: {config['configuration']['priority']}")
print(f" Optimization Tips:")
for tip in config['optimization_tips'][:2]: # Show first 2 tips
print(f" - {tip}")
print()
# advanced_optimization_example()Multi-Platform Deployment Strategies
python
import platform
import subprocess
import json
import os
from typing import Dict, List, Optional, Union
from abc import ABC, abstractmethod
class BitNETDeploymentStrategy(ABC):
"""Abstract base class for BitNET deployment strategies"""
@abstractmethod
def setup_environment(self) -> bool:
"""Setup deployment environment"""
pass
@abstractmethod
def deploy_model(self, model_path: str) -> bool:
"""Deploy BitNET model"""
pass
@abstractmethod
def test_deployment(self) -> Dict[str, any]:
"""Test deployment functionality"""
pass
@abstractmethod
def get_performance_metrics(self) -> Dict[str, any]:
"""Get platform-specific performance metrics"""
pass
class BitNETCppDeployment(BitNETDeploymentStrategy):
"""Deployment strategy using bitnet.cpp for maximum performance"""
def __init__(self, model_name: str = "microsoft/BitNet-b1.58-2B-4T-gguf"):
self.model_name = model_name
self.model_path = None
self.bitnet_path = None
def setup_environment(self) -> bool:
"""Setup bitnet.cpp environment"""
try:
# Check if bitnet.cpp is available
result = subprocess.run(
["python", "setup_env.py", "--help"],
capture_output=True,
text=True,
timeout=10
)
if result.returncode == 0:
print("bitnet.cpp environment detected")
return True
else:
print("bitnet.cpp not found. Installing...")
return self._install_bitnet_cpp()
except (subprocess.TimeoutExpired, FileNotFoundError):
print("bitnet.cpp not available. Please install manually.")
return False
def _install_bitnet_cpp(self) -> bool:
"""Install bitnet.cpp (simplified example)"""
try:
# Download model if needed
download_cmd = [
"huggingface-cli", "download",
self.model_name,
"--local-dir", f"models/{self.model_name.split('/')[-1]}"
]
subprocess.run(download_cmd, check=True, timeout=300)
# Setup environment
setup_cmd = [
"python", "setup_env.py",
"-md", f"models/{self.model_name.split('/')[-1]}",
"-q", "i2_s"
]
subprocess.run(setup_cmd, check=True, timeout=60)
self.model_path = f"models/{self.model_name.split('/')[-1]}/ggml-model-i2_s.gguf"
return True
except (subprocess.CalledProcessError, subprocess.TimeoutExpired) as e:
print(f"bitnet.cpp installation failed: {e}")
return False
def deploy_model(self, model_path: str = None) -> bool:
"""Deploy model with bitnet.cpp"""
if model_path:
self.model_path = model_path
if not self.model_path or not os.path.exists(self.model_path):
print("Model path not available or model not found")
return False
print(f"BitNET model deployed: {self.model_path}")
return True
def test_deployment(self) -> Dict[str, any]:
"""Test bitnet.cpp deployment"""
if not self.model_path:
return {"success": False, "error": "Model not deployed"}
try:
test_cmd = [
"python", "run_inference.py",
"-m", self.model_path,
"-p", "Hello, this is a test.",
"-n", "20",
"-temp", "0.7"
]
start_time = time.time()
result = subprocess.run(
test_cmd,
capture_output=True,
text=True,
timeout=30
)
test_time = time.time() - start_time
if result.returncode == 0:
return {
"success": True,
"response": result.stdout.strip(),
"test_time": test_time,
"framework": "bitnet.cpp"
}
else:
return {
"success": False,
"error": result.stderr,
"framework": "bitnet.cpp"
}
except subprocess.TimeoutExpired:
return {"success": False, "error": "Test timed out"}
def get_performance_metrics(self) -> Dict[str, any]:
"""Get bitnet.cpp performance metrics"""
system_info = {
"platform": platform.system(),
"architecture": platform.machine(),
"cpu_count": os.cpu_count(),
"deployment_type": "bitnet.cpp (optimized)"
}
# Estimate performance based on platform
if platform.machine().lower() in ['arm64', 'aarch64']:
estimated_speedup = "1.37x to 5.07x"
energy_reduction = "55.4% to 70.0%"
else: # x86
estimated_speedup = "2.37x to 6.17x"
energy_reduction = "71.9% to 82.2%"
return {
**system_info,
"estimated_speedup": estimated_speedup,
"estimated_energy_reduction": energy_reduction,
"memory_footprint_mb": 400,
"optimization_level": "maximum"
}
class TransformersDeployment(BitNETDeploymentStrategy):
"""Deployment strategy using Hugging Face Transformers"""
def __init__(self, model_name: str = "microsoft/bitnet-b1.58-2B-4T"):
self.model_name = model_name
self.model = None
self.tokenizer = None
def setup_environment(self) -> bool:
"""Setup Transformers environment"""
try:
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
print("Transformers environment available")
return True
except ImportError as e:
print(f"Transformers not available: {e}")
return False
def deploy_model(self, model_path: str = None) -> bool:
"""Deploy model with Transformers"""
try:
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = model_path if model_path else self.model_name
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True
)
print(f"BitNET model deployed with Transformers: {model_name}")
return True
except Exception as e:
print(f"Model deployment failed: {e}")
return False
def test_deployment(self) -> Dict[str, any]:
"""Test Transformers deployment"""
if self.model is None or self.tokenizer is None:
return {"success": False, "error": "Model not deployed"}
try:
import torch
messages = [{"role": "user", "content": "Hello, this is a test."}]
prompt = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = self.tokenizer(prompt, return_tensors="pt").to(self.model.device)
start_time = time.time()
with torch.no_grad():
outputs = self.model.generate(
**inputs,
max_new_tokens=20,
temperature=0.7,
do_sample=True,
pad_token_id=self.tokenizer.eos_token_id
)
test_time = time.time() - start_time
response = self.tokenizer.decode(
outputs[0][inputs['input_ids'].shape[1]:],
skip_special_tokens=True
)
return {
"success": True,
"response": response.strip(),
"test_time": test_time,
"framework": "transformers"
}
except Exception as e:
return {"success": False, "error": str(e)}
def get_performance_metrics(self) -> Dict[str, any]:
"""Get Transformers performance metrics"""
import torch
system_info = {
"platform": platform.system(),
"architecture": platform.machine(),
"deployment_type": "transformers (development)"
}
if torch.cuda.is_available():
system_info.update({
"cuda_available": True,
"gpu_name": torch.cuda.get_device_name(0),
"gpu_memory_gb": torch.cuda.get_device_properties(0).total_memory / 1e9
})
return {
**system_info,
"optimization_level": "standard",
"note": "Use bitnet.cpp for production efficiency gains"
}
class MultiPlatformBitNETManager:
"""Manager for multi-platform BitNET deployment"""
def __init__(self):
self.deployment_strategies = {
"bitnet_cpp": BitNETCppDeployment(),
"transformers": TransformersDeployment()
}
self.active_strategy = None
def auto_select_strategy(self) -> str:
"""Automatically select best deployment strategy"""
# Try bitnet.cpp first for production efficiency
if self.deployment_strategies["bitnet_cpp"].setup_environment():
return "bitnet_cpp"
# Fallback to transformers
if self.deployment_strategies["transformers"].setup_environment():
return "transformers"
raise RuntimeError("No suitable deployment strategy available")
def deploy_with_strategy(self, strategy_name: str, model_path: str = None) -> bool:
"""Deploy using specific strategy"""
if strategy_name not in self.deployment_strategies:
raise ValueError(f"Unknown strategy: {strategy_name}")
strategy = self.deployment_strategies[strategy_name]
if strategy.setup_environment() and strategy.deploy_model(model_path):
self.active_strategy = strategy_name
return True
return False
def comprehensive_test(self) -> Dict[str, any]:
"""Run comprehensive test across all available strategies"""
results = {}
for strategy_name, strategy in self.deployment_strategies.items():
print(f"Testing {strategy_name} deployment...")
if strategy.setup_environment():
if strategy.deploy_model():
test_result = strategy.test_deployment()
performance_metrics = strategy.get_performance_metrics()
results[strategy_name] = {
"deployment_success": True,
"test_result": test_result,
"performance_metrics": performance_metrics
}
else:
results[strategy_name] = {
"deployment_success": False,
"error": "Model deployment failed"
}
else:
results[strategy_name] = {
"deployment_success": False,
"error": "Environment setup failed"
}
return results
def get_recommendations(self) -> Dict[str, str]:
"""Get deployment recommendations based on use case"""
return {
"production": "bitnet_cpp - Maximum efficiency and performance",
"development": "transformers - Easy integration and debugging",
"mobile": "bitnet_cpp - Optimized for resource constraints",
"research": "transformers - Flexible experimentation",
"edge": "bitnet_cpp - CPU optimizations and efficiency",
"cloud": "bitnet_cpp - Cost-effective scaling"
}
# Example multi-platform deployment
def multi_platform_deployment_example():
"""Demonstrate multi-platform BitNET deployment"""
manager = MultiPlatformBitNETManager()
print("=== BitNET Multi-Platform Deployment ===\n")
# Comprehensive testing
results = manager.comprehensive_test()
print("Deployment Test Results:")
for strategy, result in results.items():
print(f"\n{strategy.upper()}:")
if result["deployment_success"]:
test = result["test_result"]
perf = result["performance_metrics"]
print(f" ✅ Deployment: Success")
print(f" ✅ Test: {'Success' if test['success'] else 'Failed'}")
print(f" 📊 Platform: {perf.get('platform', 'Unknown')}")
print(f" 🚀 Optimization: {perf.get('optimization_level', 'Standard')}")
if 'estimated_speedup' in perf:
print(f" ⚡ Speedup: {perf['estimated_speedup']}")
else:
print(f" ❌ Deployment: Failed - {result['error']}")
# Show recommendations
print("\n=== Deployment Recommendations ===")
recommendations = manager.get_recommendations()
for use_case, recommendation in recommendations.items():
print(f"{use_case.capitalize()}: {recommendation}")
# Auto-select best strategy
try:
best_strategy = manager.auto_select_strategy()
print(f"\n🎯 Recommended Strategy: {best_strategy}")
if manager.deploy_with_strategy(best_strategy):
print(f"✅ Successfully deployed with {best_strategy}")
except RuntimeError as e:
print(f"❌ Auto-selection failed: {e}")
# multi_platform_deployment_example()