TensorRT-LLM推理优化实战

Posted on 五月 10, 2024

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TensorRT-LLM推理优化实战

引言

大语言模型（LLM）的部署面临显存占用大、推理延迟高的挑战。TensorRT-LLM提供了一套完整的优化方案，使LLM推理效率提升数倍甚至数十倍。

TensorRT-LLM架构

核心组件

class TensorRTLLMOptimizer:
    """TensorRT-LLM优化器"""
    
    def __init__(self, model_path):
        self.model_path = model_path
        self.engine_builder = EngineBuilder()
        self.quantizer = Quantizer()
        self.optimizer = Optimizer()
        
    def build_engine(self, precision='FP16'):
        # 1. 模型转换
        model = self.load_model(self.model_path)
        
        # 2. 图优化
        optimized = self.optimizer.optimize(model)
        
        # 3. 构建引擎
        engine = self.engine_builder.build(optimized, precision)
        
        return engine

量化技术

INT8量化

class INT8Quantizer:
    """INT8量化器"""
    
    def __init__(self):
        self.calibrator = CalibrationDataset()
        
    def quantize(self, model):
        # 校准
        scales = self.calibrator.calibrate(model)
        
        # 量化
        quantized_model = self.apply_quantization(model, scales)
        
        return quantized_model
    
    def apply_quantization(self, model, scales):
        for layer in model.layers:
            if isinstance(layer, Linear):
                weight_scale = scales[layer.name]['weight']
                activation_scale = scales[layer.name]['activation']
                
                # INT8量化
                layer.weight_quantized = (layer.weight / weight_scale).round()
                layer.quantized = True
                
        return model

注意力机制优化

Paged Attention

class PagedAttention:
    """分页注意力机制"""
    
    def __init__(self, block_size=16):
        self.block_size = block_size
        self.cache_manager = KVCacheManager(block_size)
        
    def forward(self, q, k, v, max_length):
        # KV缓存管理
        k_cache, v_cache = self.cache_manager.get()
        
        # 分页处理
        num_blocks = (max_length + self.block_size - 1) // self.block_size
        
        for i in range(num_blocks):
            block_k = k[:, :, i*self.block_size:(i+1)*self.block_size]
            block_v = v[:, :, i*self.block_size:(i+1)*self.block_size]
            
            # 计算注意力
            attn_out = self.compute_attention(q, block_k, block_v)
            
        return attn_out

推理配置

# TensorRT-LLM配置示例
config = {
    'model_name': 'llama-7b',
    'precision': 'FP16',
    'tensor_parallel': 1,
    'num_layers': 32,
    'num_heads': 32,
    'hidden_size': 4096,
    'vocab_size': 32000,
    'max_seq_len': 4096,
    'batch_size': 1,
    'use_flash_attention': True,
    'enable_chunked_prefill': True
}

# 构建引擎
engine = tensorrt_llm.build(model_config=config)

性能对比

配置	吞吐量	延迟	显存
原生PyTorch FP16	10 tok/s	100ms	16GB
TensorRT FP16	50 tok/s	20ms	14GB
TensorRT INT8	80 tok/s	12ms	10GB
TensorRT INT4	120 tok/s	8ms	6GB

总结

TensorRT-LLM通过量化、注意力优化、内存管理等多种技术显著提升LLM推理性能。

参考：NVIDIA TensorRT-LLM官方文档