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 8.6.10 product package.
1. TensorRT for DRIVE OS
1.1. DRIVE OS Linux "Standard"
1.2. DRIVE OS QNX "Standard"
1.4. DRIVE OS for Safety Proxy
- 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).
- Headers allow applications to compile against the proxy runtime and the 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. Breaking API Changes
- A new nvinfer1::safe::IPluginRegistry interface is introduced, which supersedes the existing nvinfer1::IPluginRegistry interface for users of the proxy and safety runtime. This change only affects users of the proxy and safety runtime. All other users can continue to use the existing nvinfer1::IPluginRegistry. Note that the return type of nvinfer1::safe::getSafePluginRegistry() was changed from nvinfer1::IPluginRegistry to nvinfer1::safe::IPluginRegistry.
- The new API nvinfer1::getBuilderSafePluginRegistry() was introduced, which supersedes the existing nvinfer1::getBuilderPluginRegistry() API for users of the proxy and safety runtime. This change only affects users of the proxy and safety runtime. All other users can continue to use the existing nvinfer1::getBuilderPluginRegistry() API.
2.2. Planned Upcoming Changes
FloatingPointErrorInformation Update
The TensorRT safety and proxy runtimes will replace FloatingPointErrorInformation with a more generalized RunTimeErrorInformation. The RunTimeErrorInformation will provide a more generalized method for async error reporting at runtime. Note that you will be able to use the same API interface to interact with the new struct but the underlying structure will be changed to a bitmap to support more types of runtime error such as Gather out of bound. The bitmap will set a flag when a supported error type occurs in the runtime instead of counting the number of errors like the old FloatingPointErrorInformation.
3. New Features and Enhancements
API Changes
Interface | Impact |
---|---|
Safety Plugin Registry Interface Updates |
Affected: A new API for the proxy runtime and safety runtime: getBuilderSafePluginRegistry(). Action: Refer to the Breaking API Changes section and the NVIDIA TensorRT 8.6.10 API Reference for DRIVE OS document to see the safety plugin registry interface updates. |
TensorRT Standard Build
The TensorRT 8.6 release includes changes to the TensorRT 8.6.0 Early Access (EA) standard builder and runtime that appear in TensorRT for DRIVE OS 6.0. For more information, refer to the NVIDIA TensorRT 8.6.0 EA Release Notes.
Documentation Changes
- The NVIDIA TensorRT 8.6.10 Developer Guide for DRIVE OS is based on the enterprise TensorRT 8.6.0 Early Access (EA) release. We have modified the TensorRT 8.6.0 EA Developer Guide documentation for DRIVE OS 6.0.7 accuracy. The TensorRT safety content has been removed.
- The TensorRT safety content is in the NVIDIA TensorRT 8.6.10 Safety Developer Guide Supplement for DRIVE OS. Refer to this PDF for all TensorRT safety specific documentation.
IMatrixMultiplyLayer Support
The TensorRT 8.6.10 release supports a new layer - IMatrixMultiplyLayer, which extends the support of ONNX GEMM / MatMul operators to support non-constant inputs for both inputs. Refer to the NVIDIA TensorRT 8.6.10 API Reference for DRIVE OS or the NVIDIA TensorRT Operator’s Reference documentation to get more information and limitations.
Minimum Tensor Rank Scope Expansion
- The input and output tensor minimum rank of IElementWiseLayer: 1.
- The input tensor minimum rank of IShuffleLayer: 0.
Refer to the NVIDIA TensorRT 8.6.10 Safety Developer Guide Supplement for DRIVE OS to get detailed input and output tensor minimum rank restrictions for each ILayer.
4. Fixed Issues
5. Known Limitations
6. Known Issues
Feature | Module | Description |
---|---|---|
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. Is it for Standard/Safety, SDK/PDK? Standard, SDK |
3988897 | TensorRT runtime |
What is the issue? The INT8 accuracy of the safety runtime decreased ~5% in the Top1/Top5 results compared to the standard runtime for some networks such as ResNet, DenseNet, and GoogleNet. How does it impact the customer? The INT8 inference by the safety runtime may have a lower accuracy compared to the standard runtime. If there is a workaround, what is it? N/A When can we expect the fix? This issue is expected to be fixed in a future release. Is it for Standard/Safety, SDK/PDK? Safety, SDK |
3995364 | DLA |
What is the issue? Setting the DLA SRAM pool size to 0 can cause hangs or memory faults. How does it impact the customer? It may not be possible to build or run DLA loadables with an SRAM pool size of 0. If there is a workaround, what is it? Set the SRAM pool size to at least 4 KiB. When can we expect the fix? This issue is expected to be fixed in a future release. Is it for Standard/Safety, SDK/PDK? Safety, Standard PDK |
4001076 | TensorRT builder |
What is the issue? ASCII control characters are not written correctly using unicode escape sequences for JSON writers. How does it impact the customer? JSON files containing ASCII control characters can not be imported correctly using the Python built-in JSON parser. This also impacts the TRex tool’s ability to import such a JSON file. If there is a workaround, what is it? Replace the
unsupported control character using the following UNIX
command:
sed 's/\x1E//g' incorrect.json >correct.json When can we expect the fix? This issue is expected to be fixed in a future release. Is it for Standard/Safety, SDK/PDK? Standard, Safety PDK |
7. TensorRT Release Properties
Linux x86-64 | Linux AArch64 | QNX AArch64 | ||
---|---|---|---|---|
QNX Safety | QNX Standard | |||
Supported NVIDIA CUDA® versions | 11.4.22 | 11.4.22 | 11.4.22 | 11.4.22 |
Supported NVIDIA cuDNN versions | 8.9.0 | 8.9.0 | No | 8.9.0 |
TensorRT Python API | Yes | Yes | No | No |
NvUffParser | Deprecated | Deprecated | No | Deprecated |
NvOnnxParser | Yes | Yes | No | Yes |
7.1. Hardware Precision
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
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
- CUDA 11.4.22
- cuDNN 8.9.0
- TensorFlow 1.15.5
- PyTorch 1.13.1
- ONNX 1.12.0 and opset 13
- DLA 3.13
- ElementWise 2.6.8
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