LLM 并行处理实战：提升处理效率的关键技术

核心要点

掌握 LLM 应用中的并行处理策略
实现高效的批量处理机制
构建可扩展的文档处理系统
优化系统性能和资源利用

并行处理的适用场景

在 LLM 应用中，以下场景特别适合使用并行处理：

批量文档处理
多模型并行推理
大规模数据分析
实时流处理

批处理策略设计

1. 基础架构

from typing import List, Dict, Any
from dataclasses import dataclass
import asyncio
from langchain.chat_models import ChatOpenAI
from langchain.callbacks import AsyncCallbackHandler

@dataclass
class BatchConfig:
    """批处理配置"""
    batch_size: int = 5
    max_concurrent_tasks: int = 3
    timeout_seconds: int = 30
    retry_attempts: int = 2

class BatchProcessor:
    def __init__(self, config: BatchConfig):
        self.config = config
        self.llm = ChatOpenAI(
            temperature=0,
            request_timeout=config.timeout_seconds
        )
        self.semaphore = asyncio.Semaphore(
            config.max_concurrent_tasks
        )
    
    async def process_batch(
        self, 
        items: List[Any]
    ) -> List[Dict]:
        """批量处理主函数"""
        batches = self._create_batches(items)
        results = []
        
        for batch in batches:
            batch_results = await self._process_batch_with_semaphore(
                batch
            )
            results.extend(batch_results)
            
        return results

2. 异步处理实现

class AsyncBatchProcessor(BatchProcessor):
    async def _process_single_item(
        self, 
        item: Any
    ) -> Dict:
        """处理单个项目"""
        async with self.semaphore:
            for attempt in range(self.config.retry_attempts):
                try:
                    return await self._execute_processing(item)
                except Exception as e:
                    if attempt == self.config.retry_attempts - 1:
                        return self._create_error_response(item, e)
                    await asyncio.sleep(2 ** attempt)
    
    async def _execute_processing(
        self, 
        item: Any
    ) -> Dict:
        """执行具体的处理逻辑"""
        task = asyncio.create_task(
            self.llm.agenerate([item])
        )
        try:
            result = await asyncio.wait_for(
                task,
                timeout=self.config.timeout_seconds
            )
            return {
                "status": "success",
                "input": item,
                "result": result
            }
        except asyncio.TimeoutError:
            task.cancel()
            raise

实战案例：批量文档处理系统

1. 系统架构

class DocumentBatchProcessor:
    def __init__(self):
        self.config = BatchConfig(
            batch_size=10,
            max_concurrent_tasks=5
        )
        self.processor = AsyncBatchProcessor(self.config)
        self.results_manager = ResultsManager()
    
    async def process_documents(
        self, 
        documents: List[str]
    ) -> Dict:
        """处理文档批次"""
        try:
            preprocessed = await self._preprocess_documents(
                documents
            )
            results = await self.processor.process_batch(
                preprocessed
            )
            return await self.results_manager.merge_results(
                results
            )
        except Exception as e:
            return self._handle_batch_error(e, documents)

2. 资源控制机制

class ResourceController:
    def __init__(self):
        self.token_limit = 4096
        self.request_limit = 100
        self._request_count = 0
        self._token_count = 0
        self._reset_time = None
    
    async def check_limits(self) -> bool:
        """检查资源限制"""
        await self._update_counters()
        return (
            self._request_count < self.request_limit and
            self._token_count < self.token_limit
        )
    
    async def track_usage(
        self, 
        tokens_used: int
    ):
        """跟踪资源使用"""
        self._token_count += tokens_used
        self._request_count += 1
        
    async def wait_if_needed(self):
        """必要时等待资源释放"""
        if not await self.check_limits():
            wait_time = self._calculate_wait_time()
            await asyncio.sleep(wait_time)

3. 结果合并策略

class ResultsManager:
    def __init__(self):
        self.merge_strategies = {
            "text": self._merge_text_results,
            "embeddings": self._merge_embedding_results,
            "classifications": self._merge_classification_results
        }
    
    async def merge_results(
        self, 
        results: List[Dict]
    ) -> Dict:
        """合并处理结果"""
        merged = {
            "success_count": 0,
            "error_count": 0,
            "results": []
        }
        
        for result in results:
            if result["status"] == "success":
                merged["success_count"] += 1
                merged["results"].append(
                    await self._process_result(result)
                )
            else:
                merged["error_count"] += 1
        
        return merged

性能优化指南

1. 内存管理

class MemoryManager:
    def __init__(self, max_memory_mb: int = 1024):
        self.max_memory = max_memory_mb * 1024 * 1024
        self.current_usage = 0
    
    async def monitor_memory(self):
        """监控内存使用"""
        import psutil
        process = psutil.Process()
        memory_info = process.memory_info()
        
        if memory_info.rss > self.max_memory:
            await self._trigger_memory_cleanup()
    
    async def _trigger_memory_cleanup(self):
        """触发内存清理"""
        import gc
        gc.collect()

2. 性能监控

class PerformanceMonitor:
    def __init__(self):
        self.metrics = {
            "processing_times": [],
            "error_rates": [],
            "throughput": []
        }
    
    async def record_metrics(
        self, 
        batch_size: int, 
        duration: float, 
        errors: int
    ):
        """记录性能指标"""
        self.metrics["processing_times"].append(duration)
        self.metrics["error_rates"].append(errors / batch_size)
        self.metrics["throughput"].append(
            batch_size / duration
        )