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推荐系统推理优化

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  • 推荐系统推理优化
    • 推荐系统(RecSys) – “沉默的大多数”
    • RecSys黑盒
      • 输入-输出
      • KPI
    • RecSys算法模型
      • RecSys算法分类
      • DNN RecSys模型范式
      • 典型DNN RecSys模型
        • WDL
        • DIN
        • DIEN
        • DLRM
    • DNN RecSys模型特征
      • Small Tensor + Big Model
      • Tensor Operations matter
      • Workload Heterogeneity
    • RecSys workload性能优化
      • Overview
      • 模型优化
        • 优化Principles
        • Tensor Operation Sub-graph
        • 涉及的优化principles
        • Case Studies
        • FC&Attention Sub-graph
        • Sub-graph fusion
          • MatMul + BiasAdd + Activation
          • Multi-Head Attention
        • Operator optimization
          • Increase Computation Intensity
          • Increase Peak Memory BW
          • Example
      • 部署优化
        • Problem statement
        • 前期探索
          • Facebook
          • 其他
    • Micro-Architecture探索
    • References

推荐系统(RecSys) – “沉默的大多数”

互联网企业

  • “在阿里和很多互联网企业中有一个“沉默的大多数”的应用,就是推荐系统:它常常占据了超过80%甚至90%的机器学习算力。”

  • Facebook AI cycles allocation
    推荐系统占据了Facebook 50%的AI训练算力,80%的AI推理算力。
    推荐系统推理优化

算力提供商

  • NV CSP Representative Workload Mix
    推荐系统推理优化

RecSys黑盒

输入-输出

在给定用户和用户上下文(如入口、时间、地域、用户的人口统计学数据等)的情况下,计算用户与库存(如商品、文章、用户等)发生交互(如点击、购买、连接等)的概率,并筛选最有可能推荐系统推理优化个库存推荐给用户,促成交互和转化。

推荐系统推理优化

KPI

  • 算法KPI – 开源
    提高用户对推荐结果的交互率和转化率,这个是算法研究的范畴。

  • 性能KPI – 可用+节流
    Latency-Bound Throughput,在满足要求的延时SLA(Service Level Agreement)的条件下,提高系统的吞吐。这个是系统的范畴。
    推荐系统推理优化
    如:
    推荐系统推理优化

RecSys算法模型

RecSys算法分类

算法设计上,大致可以按下图来划分。目前主流工业使用以DNN models为主,这也是本文的目标workload

推荐系统推理优化

DNN RecSys模型范式

DNN RecSys Model = Feature Engineering + Feature Interaction + Predictor DNN
不同的feature engineering, feature interaction和predictor DNN的选型造就了不同的模型和workload特性。

推荐系统推理优化

典型DNN RecSys模型

  • Wide and Deep Learning (WDL)

  • Deep Interest Network (DIN)

  • Deep Interest Evolution Network (DIEN)

  • Deep Learning Recommendation Model (DLRM)

WDL

  • 算法主要思路
    Wide for memorization, deep for generalization

  • 选型

    • Feature Engineering

      • embedding_lookup

      • hash bucketing

      • slice (tensor manipulation)

      • concat (tensor manipulation)

      • dense fc

    • Feature Interaction

      • concat (tensor manipulation)

      • MLP (Multi-Layer Perception)

    • Predictor DNN

      • fc
        推荐系统推理优化

DIN

  • 算法主要思路
    Attention, weighting interaction influence with similarity

  • 选型

    • Feature Engineering

      • embedding_lookup

      • concat (tensor manipulation)

    • Feature Interaction

      • batch matrix multiplication

      • sum pooling (tensor manipulation)

      • concat (tensor manipulation)

    • Predictor DNN

      • MLP
        推荐系统推理优化

DIEN

  • 算法主要思路
    Introduce time-decay effect to attention

  • 选型

    • Feature Engineering

      • embedding_lookup

      • concat (tensor manipulation)

    • Feature Interaction

      • GRU (Gated Recurrent Unit)

      • concat (tensor manipulation)

    • Predictor DNN

      • MLP
        推荐系统推理优化

DLRM

  • 算法主要思路
    Interaction using auto-correlation

  • 选型

    • Feature Engineering

      • embedding_lookup

      • sum pooling (tensor manipulation)

      • fc

    • Feature Interaction

      • batch matrix multiplication

    • Predictor DNN

      • MLP
        推荐系统推理优化

DNN RecSys模型特征

Small Tensor + Big Model

  • Each record of Criteo TeraByte Dataset
    13 numerical features + 26 categorical feature = 156 B

  • DLRM open-source Model
    ~24 billion parameters = 96 GB, most of them are embedding tables

It leads to lower Computational Intensity than CNN workloads.

Tensor Operations matter

Tensor operations which are Embedding Lookup & Tensor Manipulation occupy a non-negligible part.
推荐系统推理优化

Workload Heterogeneity

Diverse combinations of 推荐系统推理优化 lead to workload heterogeneity.
推荐系统推理优化
推荐系统推理优化

RecSys workload性能优化

Overview

推荐系统推理优化
其中,模型优化专注于优化模型自身的性能,部署优化专注于优化模型在部署环境尤其是混部环境下的性能。

模型优化

优化Principles

  • #1. Minimize system(HW/SW) overheads

    • minimize scheduling overhead

      • minimize function calls

      • use thread pool

      • use big thread (i.e. graph fusion/stitching)

    • [accelerator cases] minimize kernel launch overhead

      • use big kernel (i.e. graph fusion)

  • #2. Roofline analysis driven TFLOPS improvement

    • improve attainable TFLOPS

      推荐系统推理优化

       

    • improve actual TFLOPS

    推荐系统推理优化
    1 – improve computational intensity by decreasing 推荐系统推理优化
    2 – improve attainable TFLOPs by improving peak memory BW
    3 – improve actual TFLOPS

Tensor Operation Sub-graph

主要优化方法

graph fusion/stitching

涉及的优化principles

  • [#1] minimize kernel launch overhead

  • [#1] minimize unnecessary bad argument check

  • [#2.2] in-register/cache computing

  • [#2.3] more parallelism

Case Studies

  • embedding_lookup fusion
    Facebook multiple embedding_lookup fusion brings 7x unit level performance improvement.
    推荐系统推理优化

  • tensor manipulation sub-graph fusion
    Feature engineering sub-graph fusion brings 2x unit level performance improvement w/ XLA CPUInstructionFusion pass.
    推荐系统推理优化

FC&Attention Sub-graph

Sub-graph fusion

MatMul + BiasAdd + Activation

“MatMul + BiasAdd + Activation” 是FC子图中的典型子图,也是graph optimizer(如TF Grappler等)一般都会实现的graph optimization pass。目前主要是基于模板匹配的方式来实现。
推荐系统推理优化
在RecSys中的一个复杂性在于,对于同一个”MatMul + BiasAdd + Activation”语义,经常会有不同子图形式,下面给出两种:
推荐系统推理优化
推荐系统推理优化
可以看到,虽然上述两个子图语义上仍然是”MatMul+BiasAdd+Activation”, 但由于形式上已经产生变化,基于模板匹配的子图融合pass对他们并不能正确地辨识和融合,需要使用更高抽象度的融合pass去辨识。实践也表明,增强的pass会给线上inference带来20%左右的latency减少。

Multi-Head Attention

Multi-Head Attention作为attention结构的基本子图,仔细分析并做极致优化是非常有必要的。
推荐系统推理优化

Operator optimization

Increase Computation Intensity
  • reduce precision: FP32 → BF16

  • reduce data traffic

    • FC: keep packed weight to amortize weight packing traffic

    • DLRM batchMatMul – only load A while compute AAT by leveraging HW transposer

    • DLRM index – de-duplicate indices
      remove 推荐系统推理优化 data traffic

Increase Peak Memory BW
  • Improve cache residence

Example

推荐系统推理优化

假想系统参数  
L2$ peak BW(TB/s) 4
HBM2e peak BW(TB/s) 0.8
BF16 peak TFLOPS 512

部署优化

Problem statement

Mixed deployment brings deployment optimization

  • Model co-location brings performance variance (noisy neighbors)

  • Optimal hardware varies across dynamic batch size([1, 100]) & different models

推荐系统推理优化

前期探索

Facebook

Facebook proposed DeepRecSched to search good deployment configurations with dry-run. Facebook的实验报告了在CPU上~2x的QPS,在GPU上~5x的QPS。
推荐系统推理优化

其他

其他探索可见《深度学习推理性能优化》 部署优化部分。

Micro-Architecture探索

主要有两个方向:

  • 近内存计算
    代表性的工作有Facebook的NMP(Near Memory Processor), 主要是通过把embedding_lookup_reduction操作放到内存模组里面来完成,从而在不提高内存的物理带宽的前提下提高有效带宽。Facebook报告了9.8x的延时减少和4.2x的吞吐提高,基于内部的embedding-dominated的模型族。
    推荐系统推理优化

  • data pipeline in SoC

    • Intel
      Intel 计划在Sapphire Rapids CPU中引入一些data accelerator IP, 如DSA(Data Streaming Accelerator)。把memory intensive的部分从CPU指令中解放出来,offload到一个专门的IP中来实现。这为实现片上data pipeline、提高workload吞吐提供了一种可能。
      推荐系统推理优化

    References

    1. DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference

    2. The Architectural Implications of Facebook’s DNN-based Personalized Recommendation

    3. Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications

    4. Cross-Stack Workload Characterization of Deep Recommendation Systems

    5. High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models

    6. Accelerating the Wide & Deep Model Workflow from 25 Hours to 10 Minutes Using NVIDIA GPUs

    7. Applying the Roofline Model for Deep Learning performance optimizations

    8. RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing

    9. MicroRec: Efficient Recommendation Inference by Hardware and Data Structure Solutions

    10. AI Matrix: A Deep Learning Benchmark for Alibaba Data Centers

    11. Deep Learning Recommendation Model for Personalization and Recommendation Systems

    12. Download Terabyte Click Logs

    13. Roofline: An Insightful Visual Performance Model for Floating-Point Programs and Multicore Architectures

    14. Roofline Model

    15. GPU Performance Background User Guide

    16. Matrix Multiplication Background User Guide

    17. 推理性能提升一倍,TensorFlow Feature Column性能优化实践

    18. Accelerate INT8 Inference Performance for Recommender Systems with Intel® Deep Learning Boost (Intel® DL Boost)

    19. Optimizing Recommendation System Inference Performance Based on GPU

    20. Deep Learning: It’s Not All About Recognizing Cats and Dogs

    21. 深度学习推理性能优化


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