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ONNX Runtime v1.0.0
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@snnn snnn released this 30 Oct 06:27
v1.0.0
b783805

Key Updates

General

  • ONNX 1.6 compatibility - operator support for all opset11 ops on CPU, including Sequence ops.
  • Free dimension override: Add ability to override free dimensions to the inputs of a model. Free dimensions are tensor shapes which aren't statically known at model author time, and must be provided at runtime. Free dimensions are most often used for the batch size of a model's inputs, allowing for customizable batch sizes at runtime. This feature enables certain optimizations since the shape can be known apriori.
  • Performance improvements to further accelerate model inferencing latency on CPU and GPU. Notable updates include:
    • Additional CUDA operators added to support Object Detection and BERT models. Note: CUDA operator coverage is still limited and performance will vary significantly depending on the model and operator usage.
    • Improved parallelism for operators that use GEMM and MatMul
    • New implementation for 64 bits MatMul on x86_64 CPU
    • Added ability to set # of threads used by intra and inter operator parallelism to allow optimal configuration for both sequential and concurrent inferencing scenarios
    • Gelu fusion optimizer
  • Threading updates:
    • Eigen ThreadPool is now the default (previously there were two thread pool implementations, TaskThreadPool and Eigen ThreadPool)
    • Ability to disable multiple threading by setting thread pool size to 1 and onnxruntime_USE_OPENMP to OFF.
    • MLAS now uses the number of thread pool threads plus one as the parallelism level. (e.g. if you have 4 CPUs, you need to set the thread pool size to 3 so that you only have one thread per CPU)
  • CPU Python package is manylinux1 compliant. The GPU Python package is manylinux2010 and compatible with CUDA 10.0/cuDNN 7.6
  • Support for CentOS 6 and 7 for Python, C, and C++. Most of the code is now C++11 compliant (previously required C++14). C# .NET Core compatibility coming soon.
  • Package for ArchLinux
  • Telemetry - component level logging through Trace Logging for Windows builds. Data collection is limited and used strictly to identify areas for improvement. You can read more about the data collected and how to manage these settings here.
  • Bug fixes to address various issues filed on Github and other channels

API updates

  • Updates to the C API for clarity of usage. The 1.0 version of the API is now stable and will maintain backwards compatibility. Versioning is in supported to accommodate future updates.
  • C APIs are ABI compatible and follows Semantic Versioning. Programs linked with the current version of the ONNX Runtime library will continue to work with subsequent releases without updating any client code or re-linking.
  • New session option available for serializing optimized ONNX models
  • Enabled some new capabilities through the Python and C# APIs for feature parity, including registration of execution providers in Python and setting additional run options in C#.

Execution Providers (EP)

Updates

  • General Availability of the OpenVINO™ EP for Intel® CPU, Intel® Integrated Graphics, Intel® Neural Compute Stick 2, and the Intel® Vision Accelerator Design with Intel® Movidius™ Myriad™ VPU powered by OpenVINO™nGraph EP support of new operators.
  • MKL-DNN EP updated from 0.18.1 to 1.0.2 for an average of 5-10% (up to 50%) performance improvement on ONNX Model Zoo model latency
  • nGraph EP updated from 0.18 to 0.26, with support of new operators for quantization and performance improvements on LSTM ops (without peephole) and Pad op
  • TensorRT EP updated to the latest TensorRT 6.0 libraries
  • Android DNNLibrary version update

New EP support

  • [Preview] NUPHAR (Neural-network Unified Preprocessing Heterogeneous ARchitecture) is a TVM and LLVM based EP offering model acceleration by compiling nodes in subgraphs into optimized functions via JIT
  • [Preview] DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning on Windows, providing GPU acceleration for common machine learning tasks across a broad range of supported hardware and drivers
  • [Preview] Support for Intel® Vision Accelerator Design with Intel® Arria™ 10 FPGA powered by OpenVINO™.
  • [Preview] ARM Compute Library (ACL) Execution Provider targets ARM CPUs and GPUs for optimized execution of ONNX operators using the low-level libraries.

Build updates

  • Two new cmake options: onnxruntime_USE_GEMMLOWP, onnxruntime_USE_AUTOML, onnxruntime_USE_DML
  • Removed two cmake options: onnxruntime_USE_MLAS/onnxruntime_USE_EIGEN_THREADPOOL. These are always ON now.
  • The minimal supported gcc version is 4.8.2

Tooling

  • Availability of ONNX Go Live tool, which automates the process of shipping ONNX models by combining model conversion, correctness tests, and performance tuning into a single pipeline as a series of Docker images.
  • Updates to the quantization tool
    • Supports selective quantization for some nodes instead of all possible nodes
    • Bias quantization for Conv nodes
    • Node fusion for dynamic quantization
  • onnxruntime_perf_tool usage updates:
    • new option "-y" for controlling inter_op_num_threads
    • max optimization level is now 99, and 3 is now an invalid value. In most cases, this tool should be run with "-o 99"

Other Dependency Updates

  • Replaced gsl with gsl-lite to be compatible with C++11
  • Added NVIDIA cub
  • Added Wil for DML execution provider
  • Pybind11 updated from 2.2.4 to 2.4.0 to fix a compatibility issue with Baidu PaddlePaddle and some other python modules that are also depend on Pybind11
  • TVM updated to a newer version
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