NVIDIA TensorRT for DRIVE OS
NVIDIA TensorRT 8.5.10 Release Notes for DRIVE OS (PDF) - Last updated February 3, 2023

Revision History

This is the revision history of the NVIDIA TensorRT 8.5.10 Release Notes for DRIVE OS.

Document revision history

Date Summary of Change
October 25, 2022 Initial draft
October 26, 2022 Start of review
December 9, 2022 End of review
December 12, 2022 Approval review

Abstract

NVIDIA TensorRT is a C++ library that facilitates high performance inference on NVIDIA GPUs. It is designed to work in connection with deep learning frameworks that are commonly used for training. TensorRT focuses specifically on running an already trained network quickly and efficiently on a GPU for the purpose of generating a result; also known as inferencing. These release notes describe the key features, software enhancements and improvements, and known issues for the TensorRT 8.5.10 product package.

1. TensorRT for DRIVE OS

1.1. DRIVE OS Linux "Standard"

The NVIDIA® TensorRT™ 8.5.10 for DRIVE® OS release includes a TensorRT Standard+Proxy package. The Standard+Proxy package for NVIDIA DRIVE OS users of TensorRT, which is available on all platforms except QNX safety, contains the builder, standard runtime, proxy runtime, consistency checker, parsers, Python bindings, sample code, standard and safety headers, and documentation. The builder can create engines suitable for the standard runtime and DLA. This release includes safety headers and the capability to build standard engines restricted to the scope of operations that will be supported by the safety and proxy runtimes in this and future NVIDIA DRIVE OS 6.0 releases.

1.2. DRIVE OS QNX "Standard"

The NVIDIA TensorRT 8.5.10 for DRIVE OS release includes a TensorRT Standard+Safety Proxy package. The Standard+Safety Proxy package for NVIDIA DRIVE OS users of TensorRT contains the builder, standard runtime, proxy runtime, consistency checker, parsers, Python bindings, sample code, standard and safety headers, and documentation. The builder can create engines suitable for the standard runtime, GPU, and DLA.

DRIVE OS QNX for Safety

The safety package is available in the NVIDIA DRIVE OS 6.0.6.0 release. The safety package for NVIDIA DRIVE OS users of TensorRT, which is only available on QNX safety, contains the safety runtime, safety headers only, and the API documentation specific to the safety runtime.

1.3. DRIVE OS for Safety Proxy

Proxy runtime
The TensorRT proxy runtime is a version of the safety runtime for platforms that are not safety certified. This includes NVIDIA DRIVE OS x86 SDK, NVIDIA DRIVE OS Linux SDK, NVIDIA DRIVE OS Linux PDK, NVIDIA DRIVE OS QNX SDK and NVIDIA DRIVE OS QNX PDK. The proxy runtime is part of the development flow for safety but it is not certified itself. The proxy runtime only supports engines with engine capability kSAFETY (safe engines).
Safety headers
Headers allow applications to compile against the proxy runtime and the safety runtime.
Safety runtime
The safety runtime is also a library that allows applications to load serialized engine plans and perform inference. It is only available for QNX safety. The safety runtime only supports engines with engine capability kSAFETY (safe engines).

2. Release Highlights

2.1. Deprecations in this Release

The following items have been deprecated in this release.
Note: These deprecations are still supported in this current release. They will be removed in the next major TensorRT 9.0 release.
Table 1. Deprecations in TensorRT 8.5.10
Summary Impact
Two DLA Safety samples, dlaSafetyBuilder and dlaSafetyRuntime have been removed from the TensorRT 8.5.10 release.

Module: TensorRT samples

Action: Since TensorRT safety runtime does not support DLA, this removal does not have any impact for TensorRT safety users. You can check TensorRT standard runtime for the DLA usage.

ICudaEngine::getNbBindings

Module: TensorRT runtime

Action: Since the TensorRT standard runtime was updated to enqueueV3() in the TensorRT 8.5.1 release, the enqueueV3() in the TensorRT safety runtime reduces the API changes when migrating from the standard runtime to the safety runtime. Name-based functions have been added to safe::ICudaEngine.

ICudaEngine::getBindingIndex
ICudaEngine::getBindingName
ICudaEngine::bindingIsInput
ICudaEngine::getBindingDimensions
ICudaEngine::getBindingDataType
ICudaEngine::getBindingBytesPerComponent
ICudaEngine::getBindingComponentsPerElement
ICudaEngine::getBindingFormat
ICudaEngine::getBindingVectorizedDim
IExecutionContext::getStrides

Module: TensorRT safety execution context

IExecutionContext::enqueueV2

2.2. Planned Upcoming Changes

The following sections describe planned, upcoming changes for a future release.

IGatherLayer Support

The TensorRT safety and proxy runtimes will add support for IGatherLayer in a future release. This is to complement the IGatherLayer functionality in the TensorRT standard runtime. IGatherLayer support in the TensorRT safety and proxy runtimes will be designed to support the Gather mode for the first DRIVE OS 6.0 safety cycle. Refer to the NVIDIA TensorRT 8.5.10 API Reference for DRIVE OS or the NVIDIA TensorRT Operator’s Reference documentation to get more information and limitations.

IMatrixMultiplyLayer Support

TensorRT will add support for IMatrixMultiplyLayer in a future release. This will allow users of TensorRT safety to use the ONNX GEMM or MatMul operators as inputs to the TensorRT builder. Refer to the NVIDIA TensorRT 8.5.10 API Reference for DRIVE OS or the NVIDIA TensorRT Operator’s Reference documentation to get more information and limitations.

Safe Plugin Registry Interface Updates

In a future release, TensorRT will split the existing nvinfer1::IPluginRegistry interface in NvinferRuntimeCommon.h into two new interfaces:
  • nvinfer1::IPluginRegistry in NvInferRuntime.h, which will be used in TensorRT standard.
  • nvinfer1::safe::IPluginRegistry in NvInferSafeRuntime.h, which will be used in TensorRT safety.
The two interfaces will be completely separate and shall not derive from a common base class.
Standard code that uses nvinfer1::IPluginRegistry is expected to compile without modifications. Automotive safety code that uses plugin registries may require the following changes to continue to compile:
  • The explicit use of nvinfer1::IPluginRegistry must be replaced by nvinfer1::safe::IPluginRegistry.
  • Instead of directly including the NvInferRuntimeCommon.h header, the user code must include NvInferSafeRuntime.h instead.
  • The use of getBuilderPluginRegistry() should be replaced by getBuilderSafePluginRegistry().

3. New Features and Enhancements

This release includes support for these new features and enhancements.

API Changes

The following table provides a summary of the TensorRT API changes for the NVIDIA DRIVE OS 6.0.6 release. Any changes that affect the safety runtime will also affect the proxy runtime.
Table 2. API Changes for DRIVE OS 6.0.6
Interface Impact
ICudaEngine::getTensorShape

Affected: Binding index-based functions have been deprecated.

Name-based functions have been added.

Action: Refer to the NVIDIA TensorRT 8.5.10 API Reference for DRIVE OS.

Use enqueueV3() for asynchronous inference execution.

Use name-based functions.
ICudaEngine::getTensorDataType
ICudaEngine::getTensorIOMode
ICudaEngine::getTensorBytesPerComponent
ICudaEngine::getTensorComponentsPerElement
ICudaEngine::getTensorVectorizedDim
ICudaEngine::getNbIOTensors
ICudaEngine::getIOTensorName
IExecutionContext::getTensorStrides
IExecutionContext::setInputTensorAddress
IExecutionContext::setOutputTensorAddress
IExecutionContext::setInputConsumedEvent
IExecutionContext::getInputConsumedEvent
IExecutionContext::getInputTensorAddress
IExecutionContext::getOutputTensorAddress
IExecutionContext::enqueueV3

TensorRT Standard Build

The TensorRT 8.5 release includes changes to the standard builder and runtime that appear in TensorRT for DRIVE OS 6.0. For more information, refer to the TensorRT 8.5.1 Release Notes.

Documentation Changes

The TensorRT 8.5.10 documentation has been updated accordingly:
  • The NVIDIA TensorRT 8.5.10 Developer Guide for DRIVE OS is based on the enterprise TensorRT 8.5.x release. We have modified the TensorRT 8.5.x Developer Guide documentation for DRIVE OS 6.0.6 accuracy. The TensorRT safety content has been removed.
  • The TensorRT safety content is in the NVIDIA TensorRT 8.5.10 Safety Developer Guide Supplement for DRIVE OS. Refer to this PDF for all TensorRT safety specific documentation.

Safety Samples Update

New safety samples have been added to TensorRT 8.5.10. These samples focus on TensorRT safety features and functionality. Refer to the NVIDIA TensorRT 8.5.10 Safety Developer Guide Supplement for DRIVE OS for more information.

You can find the safety samples in the /usr/src/tensorrt/samples package directory. For more information on running samples, refer to the README.md file included with the sample.

Two DLA Safety samples, dlaSafetyBuilder and dlaSafetyRuntime have been removed from the TensorRT 8.5.10 release.

ISliceLayer Support

The TensorRT 8.5.10 release supports a new layer called ISliceLayer, which enables the slice operation of input tensors along all axes. Slices must be FP32, FP16, or INT8 precision and must meet the requirements listed in the NVIDIA TensorRT 8.5.10 Developer Guide for DRIVE OS. Refer to ONNX slice Op definition and the NVIDIA TensorRT 8.5.10 API Reference for DRIVE OS documentation for more information.

Equal Operation Support in IElementWiseLayer

The TensorRT 8.5.10 release extends IElementWiseLayer to support equal operation (ElementWiseOperation::kEQUAL). This is the first ElementWise logical operation that DLA supports, so there are several restrictions and requirements imposed when adopting this operation in DLA.

One such requirement is that you must explicitly set the device type of the ElementWise equal layer to DLA. trtexec now supports a flag --layerDeviceTypes to let you explicitly specify the device type for individual layers. Refer to the NVIDIA TensorRT 8.5.10 Developer Guide for DRIVE OS documentation for more information on the above changes.

Opportunistic TensorRT Safe Engine Version Forward Compatibility

Opportunistic TensorRT Safe Engine Version Forward Compatibility allows users to run safe engines generated by some older TensorRT versions with the current TensorRT safety runtime under specific conditions. This is only possible when the safety runtime does not change materially between releases, which would generally be limited to the safety and stabilization phase of a safety cycle leading up to safety assessment.

While we strive to ensure safe engine version forward compatibility opportunistically, safe engines generated from previous TensorRT versions are not forward compatible with the current TensorRT 8.5.10 safety runtime due to material changes in the runtime from new functionality. Similarly, safe engines generated from the current release will not be forward compatible with the TensorRT 8.6.10 runtime. Absent bugs and safety-related refactoring that would force us to do otherwise, our goal is to support safe engines generated from TensorRT 8.6.10+ for usage in later releases throughout the remainder of TensorRT 8 development.

Refer to the NVIDIA TensorRT 8.5.10 Safety Developer Guide Supplement for DRIVE OS and the Release Notes of each release for the supported forward compatible safe engine versions and limitations.

enqueueV3()

The TensorRT 8.5.10 release added a new function called enqueueV3() to support asynchronous inference execution. enqueueV3() adds support for constant input buffers, which is a safety requirement. Since the TensorRT standard runtime was updated to enqueueV3() in the TensorRT 8.5.1 release, the enqueueV3() in the TensorRT safety runtime reduces the API changes when migrating from the standard runtime to the safety runtime. Binding index-based functions have been deprecated and name-based functions have been added to safe::ICudaEngine. For more information regarding API changes, refer to the NVIDIA TensorRT 8.5.10 API Reference for DRIVE OS.

TensorRT Consistency Checker

The TensorRT 8.5.10 release of the consistency checker performs most checks to ensure that engines can be run in the safety runtime without invoking undefined or nondeterministic behavior. Operations within the safety scope are checked, tensor sizes and formats are checked, and inputs to each layer are analyzed to ensure no uninitialized values are read from memory. Some tactics require specialized kernels and internal data structures. Most, but not all, of these internal data structures are validated in the release.

4. Fixed Issues

The following NVIDIA DRIVE OS issues from the previous release are resolved in this release.
Table 3. Fixed Issues in TensorRT 8.5.10
Feature Module Description
3785919 TensorRT safety package When installing files from Debians on the same system, some files installed by NVIDIA DRIVE OS QNX safety TensorRT Debian would be in the same location as the NVIDIA DRIVE OS QNX proxy TensorRT Debian. This limitation has been fixed in this release.
3698033 DLA Some networks would fail to build DLA INT8 loadable in DLA_STANDALONE mode with INT8 calibrator. This bug has been fixed in this release.
3698054 TensorRT builder

In some cases, the TensorRT builder would allow input and output tensors in HWC16 format in FP16 precision. This format is outside the safety scope.

HWC16 format has been removed from available formats in this release.

3657753 TensorRT runtime There would sometimes be issues with large channel sizes with structured sparsity convolution kernels (seen at size 4096). This bug has been fixed in this release.
3689094 TensorRT builder TensorRT would take some dense weights as sparse, if they match some special pattern. This bug has been fixed in this release.
3448473 TensorRT builder The DLA compilation process in NVIDIA DRIVE OS 6.0.5.0 had a deep recursive call which required a lot of stack memory. On QNX, this may have exceeded the available stack space, leading to memory faults. This bug has been fixed in this release.

5. Known Limitations

Feature Module Description
DLA TensorRT DLA is not supported through the TensorRT safety runtime. The DLA loadables for standard and safety can be consumed by the cuDLA runtime and the NvMedia runtime.
DLA TensorRT When running on DLA, various layers have restrictions on supported parameters and input shapes. Some existing limitations for the convolution, fully connected, concatenation, and pooling layers were newly documented in this release. Refer to the NVIDIA TensorRT 8.5.10 Developer Guide for DRIVE OS for details.
DLA TensorRT When running INT8 networks on DLA using TensorRT, avoid marking intermediate tensors as network outputs to reduce quantization errors by allowing layers to be fused and retain higher precision for intermediate results.
DLA TensorRT
There are two modes of SoftMax where the mode is chosen automatically based on the shape of the input tensor, where:
  • the first mode triggers when all non-batch, non-axis dimensions are 1, and
  • the second mode triggers in other cases if valid.

The second of the two modes is supported only for DLA 3.9.0 and later. It involves approximations which may result in errors of a small degree. Also, batch size greater than 1 is supported only for DLA 3.9.0 and later.

Refer to the NVIDIA TensorRT 8.5.10 Developer Guide for DRIVE OS for details.

DLA TensorRT

The DLA compiler can remove identity transposes, but it cannot fuse multiple adjacent transpose layers into a single transpose layer. Likewise, for reshape.

For example, given a TensorRT IShuffleLayer consisting of two non-trivial transposes and an identity reshape in between, the shuffle layer will be translated into two consecutive DLA transpose layers, unless you merge the transposes together manually in the model definition in advance.

Layers TensorRT For a list of safety-specific layer limitations, refer to the NVIDIA TensorRT 8.5.10 Safety Developer Guide Supplement for DRIVE OS.
I/O Formats TensorRT When using vectorized I/O formats, the extent of a tensor in a vectorized dimension might not be a multiple of the vector length. Elements in a partially occupied vector that are not within the tensor are referred to here as vector-padding.
  • For input tensors, the application shall set vector-padding elements to zero.
  • For output tensors, the value of vector-padding elements is undefined. In a future release, TensorRT will support setting them to zero.
Safety samples TensorRT We cannot use -Xcompiler -Wno-deprecated-declarations options for safety samples; that is a standard certified option. We only add it for standard builds. Seeing the deprecated warnings during the build is expected for this case.
Execution context TensorRT The total execution context memory size is limited to 4 GiB due to internal safety constraints. Error will be reported if the engine plans require memory that is more than 4 GiB.
Execution context TensorRT Users of DRIVE OS must ensure that enqueueV3() is not called concurrently by different execution contexts created from the same engine.
Restricted mode TensorRT Users who do not set layer precisions explicitly, IBuilder::isNetworkSupported may return True and building a standard engine with the kSAFETY_SCOPE flag may pass while building a safe engine fails with the same network.

6. Known Issues

Feature Module Description
3494734 DLA

What is the issue? Some networks may produce incorrect outputs when run on DLA with large batch sizes.

How does it impact the customer? Running networks on DLA with batch sizes larger than 32 may produce incorrect outputs.

If there is a workaround, what is it? To work around this issue, use a batch size smaller than 32.

When can we expect the fix? The issue will be fixed in a future DLA release.

Is it for Standard/Safety, SDK/PDK? Standard, SDK

3656116 TensorRT runtime

What is the issue? There is an up to 7% performance regression for the 3D-UNet networks compared to TensorRT 8.4 EA when running in INT8 precision on NVIDIA Orin due to a functionality fix.

How does it impact the customer? When running 3D-UNet networks in INT8 precision, the latency will be up to 7% longer than in TensorRT 8.4 EA.

If there is a workaround, what is it? To work around this issue, set the input type and format to kINT8 and kCHW32, respectively.

When can we expect the fix? We do not plan to fix this performance regression since it was caused by a necessary fix for an accuracy issue.

Is it for Standard/Safety, SDK/PDK? Standard, SDK

3263411 TensorRT builder

What is the issue? For some networks, building and running an engine in the standard runtime will have better performance than the safety runtime. This can be due to various limitations in scope of the safety runtime including more limited tactics, tensor size limits, and operations supported in the safety scope.

How does it impact the customer? Inference in the safety runtime may be significantly slower than in the standard runtime.

If there is a workaround, what is it? Depending on the network, it may or may not be possible to reorganize operations into a more efficient form matching the safety runtime scope.

What is the recommendation? It is recommended to work with NVIDIA and provide proxy networks as early as possible that demonstrate key performance metrics close to actual production networks. Future releases will target performance improvements for networks within the safety scope.

Is it for Standard/Safety, SDK/PDK? Standard, SDK

3827883 Samples

What is the issue? The trtexec binary shipped with TensorRT has an unnecessary dependency on deprecated NVMedia libraries.

How does it impact the customer? The binary will not be usable if the deprecated NVMedia libraries are missing.

If there is a workaround, what is it? Building trtexec from source will result in a binary without the extra dependency. Refer to the samples README for details on how to do so.

When can we expect the fix? This issue is not expected to be fixed in a future release.

Is it for Standard/Safety, SDK/PDK? Safety, PDK

7. TensorRT Release Properties

The following table describes the release properties and software versions.
  Linux x86-64 Linux AArch64 QNX AArch64
QNX Safety QNX Standard
Supported NVIDIA CUDA® versions 11.4 11.4 11.4 11.4
Supported NVIDIA cuDNN versions 8.6.0 8.6.0 No 8.6.0
TensorRT Python API Yes Yes No No
NvUffParser Deprecated Deprecated No Deprecated
NvOnnxParser Yes Yes No Yes
Note: With the exception of QNX safety, which requires engines to be built and serialized on QNX standard, serialized engines are not generally portable across platforms or TensorRT versions. In the standard runtime, version numbers must match (in major, minor, patch, and build) for the previously generated serialized engine to be minimally compatible. For more information, refer to the NVIDIA TensorRT 8.5.10 Safety Developer Guide Supplement for DRIVE OS. In the NVIDIA TensorRT 8.5.10 safety runtime, version numbers for major, minor, and patch must be earlier or equal to the runtime version numbers, and later than or equal to 8.5.10.

7.1. Hardware Precision

The following table lists NVIDIA hardware and which precision modes each hardware supports. It also lists availability of Deep Learning Accelerator (DLA) on this hardware. For standard runtime, TensorRT supports SM 7.x or SM 8.x. For proxy runtime, TensorRT supports all hardware with capability of 8.x. For safety runtime, TensorRT supports hardware with capability of 8.7.
For more information, refer to the “If I build the engine on one GPU and run the engine on another GPU, will this work?” question in the FAQ section in the NVIDIA TensorRT 8.5.10 Developer Guide for DRIVE OS.
Table 4. Hardware and precision support for TensorRT 8.5.10
CUDA Compute Capability Example Device TF32 FP32 FP16 INT8 FP16 Tensor Cores INT8 Tensor Cores DLA
8.7 NVIDIA Orin

No (TensorRT safe)

Yes (TensorRT standard)

 Yes Yes Yes Yes Yes Yes
8.6 NVIDIA A10 Yes Yes Yes Yes Yes Yes No
8.0 NVIDIA PG199 Yes Yes Yes Yes Yes Yes No

7.2. Software Versions Per Platform

Table 5. Software versions per platform for TensorRT 8.5.10
Platform Compiler Version Python Version
Ubuntu 20.04 x86-64 gcc 9.3.0 3.8
Ubuntu 20.04 AArch64 gcc 9.3.0 3.8
QNX AArch64 QNX 7.1.0 Q++ 8.3.0 N/A

7.3. Compatibility

TensorRT 8.5.10 has been tested with the following:

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