推荐系统推理优化

@(Engineering Practice)[Deep Learning, Recommendation System, Inference]

姚伟峰

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 $k$ 个库存推荐给用户，促成交互和转化。

RecSys IO model

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。

RecSys Performance KPI

如：

RecSys Performance KPI Examples

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。

RecSys algorithms

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。

DNN RecSys model paradigm

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
    
    WDL

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
    
    DIN

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
    
    DIEN

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
    
    DLRM

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
~26 billion parameters = ~104 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.

time spent in Caffe2 operators in Facebook data centers

Workload Heterogeneity

Diverse combinations of $Feature\ Engineering : Feature\ Interaction : DNN\ Predictor$ lead to workload heterogeneity.

RecSys models heterogeneity

RecSys models characteristics heterogeneity

RecSys workload性能优化

Overview

optimization methods

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
  $\small \begin{align*}&attainable\_TFLOPS = min\{peak\_TFLOPS, Computational\_Intensity \times peak\_memory\_BW\} \\ &Computational\_Intensity = \frac{\#ADD + \#MUL}{(\#LOAD + \#STORE)\times word\_size}\end{align*}$
- improve actual TFLOPS
Roofline driven optimization
1 - improve computational intensity by decreasing $(\#LOAD + \#STORE)\times word\_size$
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.

Facebook embedding_lookup fusion

tensor manipulation sub-graph fusion
Feature engineering sub-graph fusion brings 2x unit level performance improvement w/ XLA CPUInstructionFusion pass.

tensor manipulation sub-graph fusion

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。

MatMul fusion

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：

MatMul subgraph variant#1

MatMul subgraph variant#2

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。

MatMul fusion brings online latency improvement

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。

MHA fusion

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 $\small output = input[ : , indices\_x, indice\_y]$ data traffic

Increase Peak Memory BW

Improve cache residence

Example

假想系统参数

L2$ peak BW(TB/s) 4

HBM2e peak BW(TB/s) 0.8

BF16 peak TFLOPS 512

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

model co-location performance variance(left), optimal hardware varies across dynamic batch size and models

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。

Facebook DeepRecSched

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。

Facebook NMP
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。
  Intel CPU + DSA

References

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推荐系统推理优化

推荐系统推理优化

推荐系统(RecSys) - “沉默的大多数”

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算力提供商

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

主要优化方法

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

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