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QDucasse / nn_benchmark

🧠 Benchmark facility to train networks on different datasets for PyTorch/Brevitas
MIT License
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Bump onnxruntime from 1.2.0 to 1.5.2 #7

Closed dependabot-preview[bot] closed 3 years ago

dependabot-preview[bot] commented 3 years ago

Bumps onnxruntime from 1.2.0 to 1.5.2.

Release notes

Sourced from onnxruntime's releases.

ONNX Runtime v1.5.2

This is a minor patch release on 1.5.1 with the following changes:

ONNX Runtime v1.5.1

Key Updates

General

  • Reduced Operator Kernel build allows ORT binaries to be built with only required operators in the model(s) - learn more
  • [Preview] ORT for Mobile Platforms - minimizes build size for mobile and embedded devices - learn more
  • Transformer model inferencing performance optimizations
    • Perf improvement for DistilBERT
    • Benchmark tool supports more pretrained models
  • Improvements in quantization tool
    • Support quantization-aware training models
    • Make calibration tool to support general preprocessing and calibrate on input
    • Simplify the quantization APIs
    • Support of model larger than 2G
  • New operators for static quantization: QLinearMul, QLinearAdd, QlinearSigmoid and QLinearLeakyRelu
  • Prepack constant matrix B for float GEMM (MatMul, Attention)
  • Limited Python 3.8 support added in addition to 3.5-3.7 for official Python packages. Not yet supported for Windows GPU and Linux ARM builds.
  • Telemetry enabled in Java and NodeJS packages for Windows builds. Note: data is not directly sent to Microsoft or ORT teams by ONNX Runtime; enabling telemetry means trace events are collected by the Windows operating system and may be sent to the cloud based on the user's privacy settings - learn more.

API

  • Python API support for RegisterCustomOpsLibrary
  • IO Binding API for C/C++/C# language bindings. This allows use of pre-allocated buffers on targeted devices and also target device for unknown output shapes.
  • Sharing of allocators between multiple sessions. This allows much better utilization of memory by not creating a separate arena for each session in the same process. See this for details.

Windows ML

  • NuGet package now supports UWP applications targeting Windows Store deployment (CPU only)
  • NuGet package now supports .NET and .NET framework applications
  • RUST Developers can now deploy Windows ML – sample and documentation available here
  • New APIs to for additional performance control:
    • IntraopNumThreads: Provides an ability to change the number of threads used in the threadpool for Intra Operator Execution for CPU operators through LearningModelSessionOptions.
    • SetNamedDimensionOverrides: Provides the ability to override named input dimensions to concrete values through LearningModelSessionOptions in order to achieve better runtime performance.
  • Support for additional ONNX format image type denotations – Gray8, normalized [0..1] and normalized [-1..1]
  • Reduced Windows ML package size by separating debug symbols into separate distribution package.

Execution Providers

  • CUDA updates
    • CUDA 10.2 / cuDNN 8.0 in official package
    • CUDA 11 support added and available to build from source
    • CUDA conv kernel support asymmetrical padding to fully support models such as YoloV3 for improved GPU perf
  • TensorRT EP updates
    • Support for TensorRT 7.1
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dependabot-preview[bot] commented 3 years ago

Superseded by #12.