Abstract

This support matrix is for TensorRT. These matrices provide a look into supported features and software for TensorRT APIs, parsers, and layers.

Important: Content that is included in <<>> brackets indicates new content from the previously published version.

For previously released TensorRT documentation, see TensorRT Archives.

1. Features For Platforms And Software

The following table lists the supported platforms and which version supports a particular feature.
Table 1.
Feature Linux x86-64 Linux aarch64 Android aarch64 QNX aarch64
Supported CUDA versions 8.0, 9.0, <<9.2>> 9.2 9.2 9.2
Supported cuDNN versions <<7.1>> 7.1 7.1 7.1
TensorRT Python API Yes No No No
NvUffParser Yes Yes Yes Yes
NvOnnxParser Yes Yes Yes Yes
Note: Serialized engines are not portable across platforms or TensorRT versions.

2. Layer Features

The following table lists the TensorRT layers, specifically, which features are supported by a particular layer.
Table 2.
Layer Dimensions of input tensor Dimensions of output tensor Does the operation apply to only the innermost 3 dimensions? Supports broadcast1 Supports broadcast across batch2
Activation 1-7 dimensions 1-7 dimensions No No No
Concatenation 1-7 dimensions 1-7 dimensions No No No
Constant 0-7 dimensions 0-7 dimensions No No Always
Convolution 3 or more dimensions 3 or more dimensions Yes No No
Deconvolution 3 or more dimensions 3 or more dimensions Yes No No
ElementWise 0-7 dimensions 0-7 dimensions No Yes Yes
FullyConnected 3 or more dimensions 3 or more dimensions Yes No No
Gather
  • Input1: 1-7 dimensions
  • Input2: 1-7 dimensions
1-7 dimensions No No Yes
LRN 3 or more dimensions 3 or more dimensions Yes No No
MatrixMultiply 2 or more dimensions 2 or more dimensions No Yes Yes
Padding 3 or more dimensions 3 or more dimensions Yes No No
Plugin User defined User defined User defined User defined User defined
Pooling 3 or more dimensions 3 or more dimensions Yes Yes Yes
RaggedSoftMax
  • Input: 2 dimensions
  • Bounds: 2 dimensions
2 or more dimensions No No Yes
Reduce 1-7 dimensions 0-7 dimensions No No No
RNN 3 dimensions 3 or more dimensions No No No
RNNv2
  • Data/Hidden/Cell: 2 or more dimensions
  • Seqlen: 0 or more dimensions
Data/Hidden/Cell: 2 or more dimensions No No No
Scale 3 or more dimensions 3 or more dimensions Yes No No
Shuffle 0-7 dimensions 0-7 dimensions No No No
SoftMax 1-7 dimensions 1-7 dimensions No No No
TopK 1-7 dimensions
  • Output1: 1-7 dimensions
  • Output2: 1-7 dimensions
Yes No Yes
Unary 0-7 dimensions 0-7 dimensions No No No
For more information about each of the TensorRT layers, see TensorRT Layers.

Notices

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1 Indicates support for broadcast in this layer. This layer allows its two input tensors to be of dimensions [1, 5, 4, 3] and [1, 5, 1, 1], and its output out be [1, 5, 4, 3]. Note: The second input tensor has been broadcast in the innermost 2 dimensions.
2 Indicates support for broadcast across the batch dimension.