LLM并行处理实践：性能增强的关键技术

要点

• 掌握LLM申请中的并行处理策略
• 实施高效的批处理机制
• 构建可扩展的文档处理系统
• 优化系统性能和资源利用率

并行处理用例

在LLM应用中，并行处理特别适合：

• 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. 基本架构

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:
    """Batch processing configuration"""
    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]:
        """Main batch processing function"""
        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:
        """Process single item"""
        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:
        """Execute specific processing logic"""
        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

真实案例：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:
        """Process document batches"""
        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:
        """Check resource limits"""
        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
    ):
        """Track resource usage"""
        self._token_count += tokens_used
        self._request_count += 1

    async def wait_if_needed(self):
        """Wait for resource release if necessary"""
        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:
        """Merge processing results"""
        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):
        """Monitor memory usage"""
        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):
        """Trigger memory cleanup"""
        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
    ):
        """Record performance metrics"""
        self.metrics["processing_times"].append(duration)
        self.metrics["error_rates"].append(errors / batch_size)
        self.metrics["throughput"].append(
            batch_size / duration
        )

最佳实践

1. 批处理优化

   • 根据系统资源动态调整批量大小
   • 实施智能重试机制
   • 监控和优化内存使用情况

2. 并发控制

   • 使用信号量限制并发
   • 实施请求速率限制
   • 设置合理的超时值

3. 错误处理

   • 实施分层错误处理
   • 记录详细的错误信息
   • 提供优雅的降级选项

性能调整点

1. 系统级

   • 监控系统资源使用情况
   • 优化内存管理
   • 实施负载平衡

2. 应用级别

   • 优化批处理策略
   • 调整并发参数
   • 实施缓存机制

概括

并行处理对于构建高性能 LLM 应用程序至关重要。要点：

• 设计高效的批处理策略
• 实施强大的资源管理
• 监控和优化系统性能
• 优雅地处理错误

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