Inference Framework based TensorRT

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应用推理引擎tensorrt部署ai算法的踩坑记录与相关技术原理学习

引言

视觉算法经过几年高速发展，大量的算法被提出。为了能真正将算法在实际应用场景中更好地应用，高性能的 inference框架层出不穷。从手机端上的ncnn到tf-lite，NVIDIA在cudnn之后，推出专用于神经网络推理的TensorRT. 经过几轮迭代，支持的操作逐渐丰富，补充的插件已经基本满足落地的需求。笔者觉得，尤其是tensorrt 5.0之后，无论是接口还是使用samples都变得非常方便集成。

NVIDIA® TensorRT™ 是一款用于高性能深度学习推理的 SDK，包括深度学习推理优化器和Runtime，可为推理应用程序提供低延迟和高吞吐量。TensorRT项目立项的时候名字叫做GPU Inference Engine（简称GIE），Tensor表示数据流动以张量的形式。所谓张量大家可以理解为更加复杂的高维数组，一般一维数组叫做Vector（即向量），二维数组叫做Matrix，再高纬度的就叫Tensor，Matrix其实是二维的Tensor。在TensoRT中，所有的数据都被组成最高四维的数组，如果对应到CNN中其实就是{N, C, H, W}，N表示batch size，即多少张图片或者多少个推断（Inference）的实例；C表示channel数目；H和W表示图像或feature maps的高度和宽度。RT表示的是Runtime.

tensorrt-optimizer

版本选型与基本概念

FP16 INT8

The easiest way to benefit from mixed precision in your application is to take advantage of the support for FP16 and INT8 computation in NVIDIA GPU libraries. Key libraries from the NVIDIA SDK now support a variety of precisions for both computation and storage.

Table shows the current support for FP16 and INT8 in key CUDA libraries as well as in PTX assembly and CUDA C/C++ intrinsics.

Feature FP16x2 INT8/16 DP4A/DP2A
PTX instructions CUDA 7.5 CUDA 8
CUDA C/C++ intrinsics CUDA 7.5 CUDA 8
cuBLAS GEMM CUDA 7.5 CUDA 8
cuFFT CUDA 7.5 I/O via cuFFT callbacks
cuDNN 5.1 6
TensorRT v1 v2 Tech Preview

PTX

PTX(parallel-thread-execution，并行线程执行) 预编译后GPU代码的一种形式，开发者可以通过编译选项 “-keep”选择输出PTX代码，当然开发人员也可以直接编写PTX级代码。另外，PTX是独立于GPU架构的，因此可以重用相同的代码适用于不同的GPU架构。 具体可参考CUDA-PDF之《PTX ISA reference document》

建议我们的CUDA 版本为CUDA 8.0以上, 显卡至少为GeForce 1060, 如果想支持Int8/DP4A等feature，还是需要RTX 1080或者P40。

TensorRT特性助力高性能算法

优化技术包括：

  • Weight & Activation Precision Calibration: 在保持精度的情况下能够量化模型到INT8，最大化吞吐
  • Layer & Tensor Fusion: 通过融合多个Op到一个kernel，优化GPU显存和IO
  • Kernel Auto-Tuning: 基于目标GPU特点，选择最佳GEMM计算方式，优化Op的计算性能
  • Dynamic Tensor Memory: 最大限度地减少内存占用并有效地为张量重用内存
  • Multi-Stream Execution: 可扩展设计以并行处理多个输入流
  • Time Fusion: 使用动态生成的Kernel优化循环神经网络

优化原理

@优化原理

网络模型的裁剪与重构

@原始网络

@vertical fusion｜

The above figures explain the vertical fusion optimization that TRT does. The Convolution (C), Bias(B) and Activation(R, ReLU in this case) are all collapsed into one single node (implementation wise this would mean a single CUDA kernel launch for C, B and R).

@horizontal fusion｜

There is also a horizontal fusion where if multiple nodes with same operation are feeding to multiple nodes then it is converted to one single node feeding multiple nodes. The three 1x1 CBRs are fused to one and their output is directed to appropriate nodes. Other optimizations Apart from the graph optimizations, TRT, through experiments and based on parameters like batch size, convolution kernel(filter) sizes, chooses efficient algorithms and kernels(CUDA kernels) for operations in network.

低精度计算的支持

  • FP16 & Int8指令的支持
  • DP4A(Dot Product of 4 8-bits Accumulated to a 32-bit)

TensorRT 进行优化的方式是 DP4A (Dot Product of 4 8-bits Accumulated to a 32-bit)，如下图：

@DP4A原理过程 这是PASCAL 系列GPU的硬件指令，INT8卷积就是使用这种方式进行的卷积计算。更多关于DP4A的信息可以参考Mixed-Precision Programming with CUDA 8

INT8 vector dot products (DP4A) improve the efficiency of radio astronomy cross-correlation by a large factor compared to FP32 computation.

@INT8 vector dot products (DP4A) improve the efficiency of radio astronomy cross-correlation by a large factor compared to FP32 computation

硬件方面Tensor Core的支持，优化卷积运算

这个需要硬件的支持，如果没有类似Volta架构的GPU就不要强求。

Framework TODO SCHEDULE

  • model load sample 模型初始化当前包括通过parser初始化和通过模型流初始化的方式。通过parser初始化过程相比较来说比较慢，因为包含parser过程
    • caffe model
    • gie model
  • plugin & extend layers
  • int8 quantity inference
    • 矫正算法的设计
    • 量化数据集合的管理，这个可以和NNIE的量化数据统一起来管理
    • 与研究侧共同确定各个层量化的范围
    • 最后更新inference模式

2022年更新

随着TensorRT的更新，当前已经支持到8.5版本，对Transformer类的支持已经获得了长足发展，尤其是对Onnx Parser的成熟与完善，使得我们在实际项目应用中能够用更少的成本来获得较大的推理性能收益。

Supported ONNX Operators

TensorRT 8.5 supports operators up to Opset 17. Latest information of ONNX operators can be found here

TensorRT supports the following ONNX data types: DOUBLE, FLOAT32, FLOAT16, INT8, and BOOL

Note: There is limited support for INT32, INT64, and DOUBLE types. TensorRT will attempt to cast down INT64 to INT32 and DOUBLE down to FLOAT, clamping values to +-INT_MAX or +-FLT_MAX if necessary.

See below for the support matrix of ONNX operators in ONNX-TensorRT.

Operator Supported Supported Types Restrictions Description
Abs Y FP32, FP16, INT32
Acos Y FP32, FP16
Acosh Y FP32, FP16
Add Y FP32, FP16, INT32
And Y BOOL
ArgMax Y FP32, FP16
ArgMin Y FP32, FP16
Asin Y FP32, FP16
Asinh Y FP32, FP16
Atan Y FP32, FP16
Atanh Y FP32, FP16
AveragePool Y FP32, FP16, INT8, INT32 2D or 3D Pooling only
BatchNormalization Y FP32, FP16
Bernoulli N
BitShift N
BlackmanWindow N
Cast Y FP32, FP16, INT32, INT8, BOOL
Ceil Y FP32, FP16
Celu Y FP32, FP16
Clip Y FP32, FP16, INT8
Compress N
Concat Y FP32, FP16, INT32, INT8, BOOL
ConcatFromSequence N
Constant Y FP32, FP16, INT32, INT8, BOOL
ConstantOfShape Y FP32
Conv Y FP32, FP16, INT8
ConvInteger N
ConvTranspose Y FP32, FP16, INT8
Cos Y FP32, FP16
Cosh Y FP32, FP16
CumSum Y FP32, FP16 axis must be an initializer
DFT N
DepthToSpace Y FP32, FP16, INT32
DequantizeLinear Y INT8 x_zero_point must be zero
Det N
Div Y FP32, FP16, INT32
Dropout Y FP32, FP16
DynamicQuantizeLinear N
Einsum Y FP32, FP16 Ellipsis and diagonal operations are not supported. Broadcasting between inputs is not supported
Elu Y FP32, FP16, INT8
Equal Y FP32, FP16, INT32
Erf Y FP32, FP16
Exp Y FP32, FP16
Expand Y FP32, FP16, INT32, BOOL
EyeLike Y FP32, FP16, INT32, BOOL
Flatten Y FP32, FP16, INT32, BOOL
Floor Y FP32, FP16
Gather Y FP32, FP16, INT8, INT32, BOOL
GatherElements Y FP32, FP16, INT8, INT32, BOOL
GatherND Y FP32, FP16, INT8, INT32, BOOL
Gemm Y FP32, FP16, INT8
GlobalAveragePool Y FP32, FP16, INT8 使用输入张量\(X\) 并对同一通道中的值应用平均池化。 这相当于内核大小等于输入张量的空间维度的 AveragePool。
GlobalLpPool Y FP32, FP16, INT8
GlobalMaxPool Y FP32, FP16, INT8
Greater Y FP32, FP16, INT32
GreaterOrEqual Y FP32, FP16, INT32
GridSample Y FP32, FP16
GRU Y FP32, FP16 For bidirectional GRUs, activation functions must be the same for both the forward and reverse pass
HammingWindow N
HannWindow N
HardSwish Y FP32, FP16, INT8
HardSigmoid Y FP32, FP16, INT8
Hardmax N
Identity Y FP32, FP16, INT32, INT8, BOOL
If Y FP32, FP16, INT32, BOOL Output tensors of the two conditional branches must have broadcastable shapes, and must have different names
ImageScaler Y FP32, FP16
InstanceNormalization Y FP32, FP16 Scales scale and biases B must be initializers. Input rank must be >=3 & <=5
IsInf N
IsNaN Y FP32, FP16, INT32
LayerNormalization N
LeakyRelu Y FP32, FP16, INT8
Less Y FP32, FP16, INT32
LessOrEqual Y FP32, FP16, INT32
Log Y FP32, FP16
LogSoftmax Y FP32, FP16
Loop Y FP32, FP16, INT32, BOOL
LRN Y FP32, FP16
LSTM Y FP32, FP16 For bidirectional LSTMs, activation functions must be the same for both the forward and reverse pass
LpNormalization Y FP32, FP16
LpPool Y FP32, FP16, INT8
MatMul Y FP32, FP16
MatMulInteger N
Max Y FP32, FP16, INT32
MaxPool Y FP32, FP16, INT8 2D or 3D pooling only. Indices output tensor unsupported
MaxRoiPool N
MaxUnpool N
Mean Y FP32, FP16, INT32
MeanVarianceNormalization Y FP32, FP16
MelWeightMatrix N
Min Y FP32, FP16, INT32
Mod Y FP32, FP16, INT32
Mul Y FP32, FP16, INT32
Multinomial N
Neg Y FP32, FP16, INT32
NegativeLogLikelihoodLoss N
NonMaxSuppression Y FP32, FP16
NonZero Y FP32, FP16
Not Y BOOL
OneHot Y FP32, FP16, INT32, BOOL
Optional N
OptionalGetElement N
OptionalHasElement N
Or Y BOOL
Pad Y FP32, FP16, INT8, INT32
ParametricSoftplus Y FP32, FP16, INT8
Pow Y FP32, FP16
PRelu Y FP32, FP16, INT8
QLinearConv N
QLinearMatMul N
QuantizeLinear Y FP32, FP16 y_zero_point must be 0
RandomNormal Y FP32, FP16 seed value is ignored by TensorRT
RandomNormalLike Y FP32, FP16 seed value is ignored by TensorRT
RandomUniform Y FP32, FP16 seed value is ignored by TensorRT
RandomUniformLike Y FP32, FP16 seed value is ignored by TensorRT
Range Y FP32, FP16, INT32
Reciprocal Y FP32, FP16
ReduceL1 Y FP32, FP16
ReduceL2 Y FP32, FP16
ReduceLogSum Y FP32, FP16
ReduceLogSumExp Y FP32, FP16
ReduceMax Y FP32, FP16
ReduceMean Y FP32, FP16
ReduceMin Y FP32, FP16
ReduceProd Y FP32, FP16
ReduceSum Y FP32, FP16
ReduceSumSquare Y FP32, FP16
Relu Y FP32, FP16, INT8
Reshape Y FP32, FP16, INT32, INT8, BOOL
Resize Y FP32, FP16 Supported resize transformation modes: half_pixel, pytorch_half_pixel, tf_half_pixel_for_nn, asymmetric, and align_corners.
Supported resize modes: nearest, linear.
Supported nearest modes: floor, ceil, round_prefer_floor, round_prefer_ceil
ReverseSequence Y FP32, FP16 Dynamic input shapes are unsupported
RNN Y FP32, FP16 For bidirectional RNNs, activation functions must be the same for both the forward and reverse pass
RoiAlign Y FP32, FP16
Round Y FP32, FP16, INT8
STFT N
ScaledTanh Y FP32, FP16, INT8
Scan Y FP32, FP16
Scatter Y FP32, FP16, INT8, INT32
ScatterElements Y FP32, FP16, INT8, INT32
ScatterND Y FP32, FP16, INT8, INT32
Selu Y FP32, FP16, INT8
SequenceAt N
SequenceConstruct N
SequenceEmpty N
SequenceErase N
SequenceInsert N
SequenceLength N
SequenceMap N
Shape Y FP32, FP16, INT32, INT8, BOOL
Shrink Y FP32, FP16, INT32
Sigmoid Y FP32, FP16, INT8
Sign Y FP32, FP16, INT8, INT32
Sin Y FP32, FP16
Sinh Y FP32, FP16
Size Y FP32, FP16, INT32, INT8, BOOL
Slice Y FP32, FP16, INT32, INT8, BOOL axes must be an initializer
Softmax Y FP32, FP16
SoftmaxCrossEntropyLoss N
Softplus Y FP32, FP16, INT8
Softsign Y FP32, FP16, INT8
SpaceToDepth Y FP32, FP16, INT32
Split Y FP32, FP16, INT32, BOOL
SplitToSequence N
Sqrt Y FP32, FP16
Squeeze Y FP32, FP16, INT32, INT8, BOOL axes must be an initializer
StringNormalizer N
Sub Y FP32, FP16, INT32
Sum Y FP32, FP16, INT32
Tan Y FP32, FP16
Tanh Y FP32, FP16, INT8
TfIdfVectorizer N
ThresholdedRelu Y FP32, FP16, INT8
Tile Y FP32, FP16, INT32, BOOL
TopK Y FP32, FP16 K input must be an initializer
Transpose Y FP32, FP16, INT32, INT8, BOOL
Trilu Y FP32, FP16, INT32, INT8, BOOL
Unique N
Unsqueeze Y FP32, FP16, INT32, INT8, BOOL axes must be a constant tensor
Upsample Y FP32, FP16
Where Y FP32, FP16, INT32, BOOL
Xor Y BOOL

Reference

附录

Init.CaffeModel

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[I] Output "prob": 1000x1x1
[I] [TRT] Applying generic optimizations to the graph for inference.
[I] [TRT] Original: 141 layers
[I] [TRT] After dead-layer removal: 141 layers
[I] [TRT] After scale fusion: 141 layers
[I] [TRT] Fusing conv1/7x7_s2 with conv1/relu_7x7
[I] [TRT] Fusing conv2/3x3_reduce with conv2/relu_3x3_reduce
。。。
[I] [TRT] Fusing inception_5b/pool_proj with inception_5b/relu_pool_proj
[I] [TRT] After vertical fusions: 84 layers
[I] [TRT] After swap: 84 layers
[I] [TRT] After final dead-layer removal: 84 layers
[I] [TRT] Merging layers: inception_3a/1x1 + inception_3a/relu_1x1 || inception_3a/3x3_reduce + inception_3a/relu_3x3_reduce || inception_3a/5x5_reduce + inception_3a/relu_5x5_reduce
[I] [TRT] Merging layers: inception_3b/1x1 + inception_3b/relu_1x1 || inception_3b/3x3_reduce + inception_3b/relu_3x3_reduce || inception_3b/5x5_reduce + inception_3b/relu_5x5_reduce
。。。
[I] [TRT] Merging layers: inception_5b/1x1 + inception_5b/relu_1x1 || inception_5b/3x3_reduce + inception_5b/relu_3x3_reduce || inception_5b/5x5_reduce + inception_5b/relu_5x5_reduce
[I] [TRT] After tensor merging: 66 layers
[I] [TRT] Eliminating contatenation inception_3a/output
[I] [TRT] Generating copy for inception_3a/1x1 + inception_3a/relu_1x1 || inception_3a/3x3_reduce + inception_3a/relu_3x3_reduce || inception_3a/5x5_reduce + inception_3a/relu_5x5_reduce to inception_3a/output
[I] [TRT] Retargeting inception_3a/3x3 to inception_3a/output
[I] [TRT] Retargeting inception_3a/5x5 to inception_3a/output
[I] [TRT] Retargeting inception_3a/pool_proj to inception_3a/output
[I] [TRT] Eliminating contatenation inception_3b/output
[I] [TRT] Generating copy for inception_3b/1x1 + inception_3b/relu_1x1 || inception_3b/3x3_reduce + inception_3b/relu_3x3_reduce || inception_3b/5x5_reduce + inception_3b/relu_5x5_reduce to inception_3b/output
[I] [TRT] Retargeting inception_3b/3x3 to inception_3b/output
[I] [TRT] Retargeting inception_3b/5x5 to inception_3b/output
[I] [TRT] Retargeting inception_3b/pool_proj to inception_3b/output
[I] [TRT] Eliminating contatenation inception_4a/output
[I] [TRT] Generating copy for inception_4a/1x1 + inception_4a/relu_1x1 || inception_4a/3x3_reduce + inception_4a/relu_3x3_reduce || inception_4a/5x5_reduce + inception_4a/relu_5x5_reduce to inception_4a/output
[I] [TRT] Retargeting inception_4a/3x3 to inception_4a/output
[I] [TRT] Retargeting inception_4a/5x5 to inception_4a/output
[I] [TRT] Retargeting inception_4a/pool_proj to inception_4a/output
[I] [TRT] Eliminating contatenation inception_4b/output
[I] [TRT] Generating copy for inception_4b/1x1 + inception_4b/relu_1x1 || inception_4b/3x3_reduce + inception_4b/relu_3x3_reduce || inception_4b/5x5_reduce + inception_4b/relu_5x5_reduce to inception_4b/output
[I] [TRT] Retargeting inception_4b/3x3 to inception_4b/output
[I] [TRT] Retargeting inception_4b/5x5 to inception_4b/output
[I] [TRT] Retargeting inception_4b/pool_proj to inception_4b/output
[I] [TRT] Eliminating contatenation inception_4c/output
[I] [TRT] Generating copy for inception_4c/1x1 + inception_4c/relu_1x1 || inception_4c/3x3_reduce + inception_4c/relu_3x3_reduce || inception_4c/5x5_reduce + inception_4c/relu_5x5_reduce to inception_4c/output
[I] [TRT] Retargeting inception_4c/3x3 to inception_4c/output
[I] [TRT] Retargeting inception_4c/5x5 to inception_4c/output
[I] [TRT] Retargeting inception_4c/pool_proj to inception_4c/output
[I] [TRT] Eliminating contatenation inception_4d/output
[I] [TRT] Generating copy for inception_4d/1x1 + inception_4d/relu_1x1 || inception_4d/3x3_reduce + inception_4d/relu_3x3_reduce || inception_4d/5x5_reduce + inception_4d/relu_5x5_reduce to inception_4d/output
[I] [TRT] Retargeting inception_4d/3x3 to inception_4d/output
[I] [TRT] Retargeting inception_4d/5x5 to inception_4d/output
[I] [TRT] Retargeting inception_4d/pool_proj to inception_4d/output
[I] [TRT] Eliminating contatenation inception_4e/output
[I] [TRT] Generating copy for inception_4e/1x1 + inception_4e/relu_1x1 || inception_4e/3x3_reduce + inception_4e/relu_3x3_reduce || inception_4e/5x5_reduce + inception_4e/relu_5x5_reduce to inception_4e/output
[I] [TRT] Retargeting inception_4e/3x3 to inception_4e/output
[I] [TRT] Retargeting inception_4e/5x5 to inception_4e/output
[I] [TRT] Retargeting inception_4e/pool_proj to inception_4e/output
[I] [TRT] Eliminating contatenation inception_5a/output
[I] [TRT] Generating copy for inception_5a/1x1 + inception_5a/relu_1x1 || inception_5a/3x3_reduce + inception_5a/relu_3x3_reduce || inception_5a/5x5_reduce + inception_5a/relu_5x5_reduce to inception_5a/output
[I] [TRT] Retargeting inception_5a/3x3 to inception_5a/output
[I] [TRT] Retargeting inception_5a/5x5 to inception_5a/output
[I] [TRT] Retargeting inception_5a/pool_proj to inception_5a/output
[I] [TRT] Eliminating contatenation inception_5b/output
[I] [TRT] Generating copy for inception_5b/1x1 + inception_5b/relu_1x1 || inception_5b/3x3_reduce + inception_5b/relu_3x3_reduce || inception_5b/5x5_reduce + inception_5b/relu_5x5_reduce to inception_5b/output
[I] [TRT] Retargeting inception_5b/3x3 to inception_5b/output
[I] [TRT] Retargeting inception_5b/5x5 to inception_5b/output
[I] [TRT] Retargeting inception_5b/pool_proj to inception_5b/output
[I] [TRT] After concat removal: 66 layers
[I] [TRT] Graph construction and optimization completed in 0.00874238 seconds.
[I] [TRT] 
[I] [TRT] --------------- Timing conv1/7x7_s2 + conv1/relu_7x7(3)
[I] [TRT] Tactic 0 time 0.370688
[I] [TRT] 
[I] [TRT] --------------- Timing conv1/7x7_s2 + conv1/relu_7x7(14)
[I] [TRT] Tactic 3146172331490511787 time 0.694752
[I] [TRT] Tactic 3528302785056538033 time 0.429056
[I] [TRT] Tactic -6618588952828687390 time 0.419296
[I] [TRT] Tactic -6362554771847758902 time 0.371168
[I] [TRT] Tactic -2701242286872672544 time 0.685056
[I] [TRT] Tactic -675401754313066228 time 0.365568
[I] [TRT] 
。。。
[I] [TRT] Tactic 5 time 0.032192
[I] [TRT] 
[I] [TRT] --------------- Timing loss3/classifier(15)
[I] [TRT] Tactic 2624962759642542471 time 0.07424
[I] [TRT] Tactic 6241535668063793554 time 0.094688
[I] [TRT] Tactic 8292480392881939394 time 0.074752
[I] [TRT] Tactic 8436800165353340181 time 0.059936
[I] [TRT] Tactic -7597689592892725774 time 0.09216
[I] [TRT] --------------- Chose 6 (5)
[I] [TRT] 
[I] [TRT] --------------- Timing prob(11)
[I] [TRT] Tactic 0 is the only option, timing skipped
[I] [TRT] Formats and tactics selection completed in 10.0197 seconds.
[I] [TRT] After reformat layers: 66 layers
[I] [TRT] Block size 1073741824
[I] [TRT] Block size 12845056
[I] [TRT] Block size 9633792
[I] [TRT] Block size 3211264
[I] [TRT] Block size 3211264
[I] [TRT] Total Activation Memory: 1102643200
[I] [TRT] Detected 1 input and 1 output network tensors.
[I] [TRT] Data initialization and engine generation completed in 0.0458818 seconds.
loadmodel time: 10322 ms
infer time: 8.20 ms

Init.GIEModel

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[I] [TRT] Glob Size is 40869280 bytes.
[I] [TRT] Added linear block of size 3211264
[I] [TRT] Added linear block of size 2408448
[I] [TRT] Added linear block of size 802816
[I] [TRT] Added linear block of size 802816
[I] [TRT] Deserialize required 13227 microseconds.
[I] googlenet_gie.bin has been successfully loaded.
loadmodel time: 36 ms
infer time: 2.80 ms

IPlugin接口中需要被重载的函数

  1. 确定输出：一个是通过int getNbOutput()得到output输出的数目，即用户所定义的一层有几个输出。另一个是通过Dims getOutputDimensions (int index, const Dims* inputs, int nbInputDims) 得到整个输出的维度信息，大家可能不一定遇到有多个输出，一般来讲只有一个输出，但是大家在做检测网络的时候可能会遇到多个输出，一个输出是实际的检测目标是什么，另一个输出是目标的数目，可能的过个输出需要设定Dimension的大小。

  2. 层配置：通过void configure() 实现构建推断（Inference） engine时模型中相应的参数大小等配置，configure()只是在构建的时候调用，这个阶段确定的东西是在运行时作为插件参数来存储、序列化/反序列化的。

  3. 资源管理：通过void Initialize()来进行资源的初始化，void terminate()来销毁资源，甚至中间可能会有一些临时变量，也可以使用这两个函数进行初始化或销毁。需要注意的是，void Initialize()和void terminate()是在整个运行时都被调用的，并不是做完一次推断（Inference）就去调用terminate。相当于在线的一个服务，服务起的时候会调用void Initialize()，而服务止的时候调用void terminate()，但是服务会进进出出很多sample去做推断（Inference）。

  4. 执行(Execution)：void enqueue()来定义用户层的操作

  5. 序列化和反序列化：这个过程是将层的参数写入到二进制文件中，需要定义一些序列化的方法。通过size_t getSerializationSize()获得序列大小，通过void serialize()将层的参数序列化到缓存中，通过PluginSample()从缓存中将层参数反序列化。需要注意的是，TensorRT没有单独的反序列化的API，因为不需要，在实习构造函数的时候就完成了反序列化的过程

  6. 从Caffe Parser添加Plugin：首先通过Parsernvinfer1::IPlugin* createPlugin()实现nvcaffeparser1::IPlugin 接口，然后传递工厂实例到ICaffeParser::parse()，Caffe的Parser才能识别

  7. 运行时创建插件：通过IPlugin* createPlugin()实现nvinfer1::IPlugin接口，传递工厂实例到IInferRuntime::deserializeCudaEngine()

TensorRT 中已经实现的Plugin

打开verbose logger之后可以看到如下输出，相关的调用接口如下:

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[V] [TRT] Plugin Creator registration succeeded - GridAnchor_TRT
[V] [TRT] Plugin Creator registration succeeded - NMS_TRT
[V] [TRT] Plugin Creator registration succeeded - Reorg_TRT
[V] [TRT] Plugin Creator registration succeeded - Region_TRT
[V] [TRT] Plugin Creator registration succeeded - Clip_TRT
[V] [TRT] Plugin Creator registration succeeded - LReLU_TRT
[V] [TRT] Plugin Creator registration succeeded - PriorBox_TRT
[V] [TRT] Plugin Creator registration succeeded - Normalize_TRT
[V] [TRT] Plugin Creator registration succeeded - RPROI_TRT
[V] [TRT] Plugin Creator registration succeeded - BatchedNMS_TRT
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extern "C" {
//!
//! \brief Create a plugin layer that fuses the RPN and ROI pooling using user-defined parameters.
//! Registered plugin type "RPROI_TRT". Registered plugin version "1".
//! \param featureStride Feature stride.
//! \param preNmsTop Number of proposals to keep before applying NMS.
//! \param nmsMaxOut Number of remaining proposals after applying NMS.
//! \param iouThreshold IoU threshold.
//! \param minBoxSize Minimum allowed bounding box size before scaling.
//! \param spatialScale Spatial scale between the input image and the last feature map.
//! \param pooling Spatial dimensions of pooled ROIs.
//! \param anchorRatios Aspect ratios for generating anchor windows.
//! \param anchorScales Scales for generating anchor windows.
//!
//! \return Returns a FasterRCNN fused RPN+ROI pooling plugin. Returns nullptr on invalid inputs.
//!
TENSORRTAPI nvinfer1::IPluginV2* createRPNROIPlugin(int featureStride, int preNmsTop,
                                                                int nmsMaxOut, float iouThreshold, float minBoxSize,
                                                                float spatialScale, nvinfer1::DimsHW pooling,
                                                                nvinfer1::Weights anchorRatios, nvinfer1::Weights anchorScales);

//!
//! \brief The Normalize plugin layer normalizes the input to have L2 norm of 1 with scale learnable.
//! Registered plugin type "Normalize_TRT". Registered plugin version "1".
//! \param scales Scale weights that are applied to the output tensor.
//! \param acrossSpatial Whether to compute the norm over adjacent channels (acrossSpatial is true) or nearby spatial locations (within channel in which case acrossSpatial is false).
//! \param channelShared Whether the scale weight(s) is shared across channels.
//! \param eps Epsilon for not diviiding by zero.
//!
TENSORRTAPI nvinfer1::IPluginV2* createNormalizePlugin(const nvinfer1::Weights* scales, bool acrossSpatial, bool channelShared, float eps);

//!
//! \brief The PriorBox plugin layer generates the prior boxes of designated sizes and aspect ratios across all dimensions (H x W).
//! PriorBoxParameters defines a set of parameters for creating the PriorBox plugin layer.
//! Registered plugin type "PriorBox_TRT". Registered plugin version "1".
//!
TENSORRTAPI nvinfer1::IPluginV2* createPriorBoxPlugin(nvinfer1::plugin::PriorBoxParameters param);

//!
//! \brief The Grid Anchor Generator plugin layer generates the prior boxes of
//! designated sizes and aspect ratios across all dimensions (H x W) for all feature maps.
//! GridAnchorParameters defines a set of parameters for creating the GridAnchorGenerator plugin layer.
//! Registered plugin type "GridAnchor_TRT". Registered plugin version "1".
//!
TENSORRTAPI nvinfer1::IPluginV2* createAnchorGeneratorPlugin(nvinfer1::plugin::GridAnchorParameters* param, int numLayers);

//!
//! \brief The DetectionOutput plugin layer generates the detection output based on location and confidence predictions by doing non maximum suppression.
//! DetectionOutputParameters defines a set of parameters for creating the DetectionOutput plugin layer.
//! Registered plugin type "NMS_TRT". Registered plugin version "1".
//!
TENSORRTAPI nvinfer1::IPluginV2* createNMSPlugin(nvinfer1::plugin::DetectionOutputParameters param);

//!
//! \brief The LReLu plugin layer performs leaky ReLU for 4D tensors. Give an input value x, the PReLU layer computes the output as x if x > 0 and negative_slope //! x if x <= 0.
//! Registered plugin type "LReLU_TRT". Registered plugin version "1".
//! \param negSlope Negative_slope value.
//!
TENSORRTAPI nvinfer1::IPluginV2* createLReLUPlugin(float negSlope);

//!
//! \brief The Reorg plugin reshapes input of shape CxHxW into a (C*stride*stride)x(H/stride)x(W/stride) shape, used in YOLOv2.
//! It does that by taking 1 x stride x stride slices from tensor and flattening them into (stridexstride) x 1 x 1 shape.
//! Registered plugin type "Reorg_TRT". Registered plugin version "1".
//! \param stride Strides in H and W, it should divide both H and W. Also stride * stride should be less than or equal to C.
//!
TENSORRTAPI nvinfer1::IPluginV2* createReorgPlugin(int stride);

//!
//! \brief The Region plugin layer performs region proposal calculation: generate 5 bounding boxes per cell (for yolo9000, generate 3 bounding boxes per cell).
//! For each box, calculating its probablities of objects detections from 80 pre-defined classifications (yolo9000 has 9416 pre-defined classifications,
//! and these 9416 items are organized as work-tree structure).
//! RegionParameters defines a set of parameters for creating the Region plugin layer.
//! Registered plugin type "Region_TRT". Registered plugin version "1".
//!
TENSORRTAPI nvinfer1::IPluginV2* createRegionPlugin(nvinfer1::plugin::RegionParameters params);

//!
//! \brief The Clip Plugin performs a clip operation on the input tensor. It
//! clips the tensor values to a specified min and max. Any value less than clipMin are set to clipMin.
//! Any values greater than clipMax are set to clipMax. For example, this plugin can be used
//! to perform a Relu6 operation by specifying clipMin=0.0 and clipMax=6.0
//! Registered plugin type "Clip_TRT". Registered plugin version "1".
//! \param layerName The name of the TensorRT layer.
//! \param clipMin The minimum value to clip to.
//! \param clipMax The maximum value to clip to.
//!
TENSORRTAPI nvinfer1::IPluginV2* createClipPlugin(const char* layerName, float clipMin, float clipMax);

//!
//! \brief The BatchedNMS Plugin performs non_max_suppression on the input boxes, per batch, across all classes.
//! It greedily selects a subset of bounding boxes in descending order of
//! score. Prunes away boxes that have a high intersection-over-union (IOU)
//! overlap with previously selected boxes. Bounding boxes are supplied as [y1, x1, y2, x2],
//! where (y1, x1) and (y2, x2) are the coordinates of any
//! diagonal pair of box corners and the coordinates can be provided as normalized
//! (i.e., lying in the interval [0, 1]) or absolute.
//! The plugin expects two inputs.
//! Input0 is expected to be 4-D float boxes tensor of shape [batch_size, num_boxes,
//! q, 4], where q can be either 1 (if shareLocation is true) or num_classes.
//! Input1 is expected to be a 3-D float scores tensor of shape [batch_size, num_boxes, num_classes]
//! representing a single score corresponding to each box.
//! The plugin returns four outputs.
//! num_detections : A [batch_size] int32 tensor indicating the number of valid
//! detections per batch item. Can be less than keepTopK. Only the top num_detections[i] entries in
//! nmsed_boxes[i], nmsed_scores[i] and nmsed_classes[i] are valid.
//! nmsed_boxes : A [batch_size, max_detections, 4] float32 tensor containing
//! the co-ordinates of non-max suppressed boxes.
//! nmsed_scores : A [batch_size, max_detections] float32 tensor containing the
//! scores for the boxes.
//! nmsed_classes :  A [batch_size, max_detections] float32 tensor containing the
//! classes for the boxes.
//!
//! Registered plugin type "BatchedNMS_TRT". Registered plugin version "1".
//!
TENSORRTAPI nvinfer1::IPluginV2* createBatchedNMSPlugin(nvinfer1::plugin::NMSParameters param);

//!
//! \brief Initialize and register all the existing TensorRT plugins to the Plugin Registry with an optional namespace.
//! The plugin library author should ensure that this function name is unique to the library.
//! This function should be called once before accessing the Plugin Registry.
//! \param logger Logger object to print plugin registration information
//! \param libNamespace Namespace used to register all the plugins in this library
//!
TENSORRTAPI bool initLibNvInferPlugins(void* logger, const char* libNamespace);

https://medium.com/@r7vme/converting-neural-network-to-tensorrt-part-1-using-existing-plugins-edd9c2b9e42a