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ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
62226d030f
* Update ONNX to 1.12 (#11924) Follow-ups that need to happen after this and before the next ORT release: * Support SequenceMap with https://github.com/microsoft/onnxruntime/pull/11731 * Support signal ops with https://github.com/microsoft/onnxruntime/pull/11778 Follow-ups that need to happen after this but don't necessarily need to happen before the release: * Implement LayerNormalization kernel for opset version 17: https://github.com/microsoft/onnxruntime/issues/11916 Fixes #11640 * Dll version fix ovep4.1 (#11953) * Setting default version values for ovep dlls as well * Update backend_manager.cc Co-authored-by: mayavijx <mayax.vijayan@intel.com> Co-authored-by: mohsin <mohsinx.mohammad@intel.com> * Optimize t5 encoder in beam search (#11926) * ooptimize t5 encoder * update * update * update * refactor expand impl * cuda tests passed * update * alignment * more alignments * review comments * Allow saving on CPU usage for infrequent inference requests by reducing thread spinning (#11841) Introduce Start/Stop threadpool spinning switch Add a session config option to force spinning stop at the end of the Run() * Restructure function inliner (#11731) * Add nested function call tests * Add overload for Specialize * Pass symboltable to onnx shape inference * Avoid renaming empty names * Enable sequence_map tests which failed before this change * Deprecate APIs returning raw ptrs and provide replacements (#11922) Provider better documentation * register signal ops for opset 17 (#11778) * Register signal ops for op set 17 Note code is mostly being moved, not added. These ops were previously only registered as Microsoft contrib ops and only built if `BUILD_MS_EXPERIMENTAL_OPS=1`. They've been added to the ai.onnx standard op set in version 17. Main components of this change: * Move the kernels from the conrib_ops directory to the core directory. * Add function bodies for ms experimental ops. This will allow old models that use the contrib ops to continue to function. All the function bodies consist of a single op (the new standard op), so performance overhead should be minimal. Minor clean-up also in this change: * De-duplicate get_scalar_value_from_tensor: put it in a new utils.h. * Fix some bugs that caused compilation errors with the experimental ops. Tested with `build.sh --ms_experimental` * Fix some spelling errors and lint violations. * Replace a couple of switch statements with `MLTypeCallDispatcher`. * Use `InlineVector` instead of `std::vector`. Unblocks https://github.com/microsoft/onnxruntime/issues/11640 * Include opset 15 in Conv+BatchNormalization fusion (#11960) * Fix WinML Tests are still targetting deprecated (deleted) experimental signal op definitions (#12006) * fix winml tests * remove legacy test * switch idft -> dft+inverse attr * upgrade opset 13->17 for signal ops tests * [C# Tests] Add support for double tensor output in TestPreTrainedModels. (#12008) Add support for double tensor output in TestPreTrainedModels. * DML EP ResNet50 opset 15 fails in ONNX checker for FusedBatchNormalization lacking training_mode attribute (#12010) FusedBatchNormalization include training_mode attribute * Generalize native op creation (#11539) * create op from ep * read input count from context * create holder to host nodes * fix typo * cast type before comparison * throw error on API fail * silence warning from minimal build * switch to unique_ptr with deleter to host nodes * fix typo * fix build err for minimal * fix build err for minimal * add UT for conv * enable test on CUDA * add comment * fix typo * use gsl::span and string view for Node constructor * Added two APIs - CopyKernelInfo and ReleaseKernelInfo * pass gsl::span by value * switch to span<NodeArg* const> to allow for reference to const containers * fix typo * fix reduced build err * fix reduced build err * refactoring node construction logic * rename exceptions * add input and output count as arguments for op creation * refactor static member * use ORT_CATCH instead of catch * cancel try catch * add static value name map * format input definition and set err code * fix comments * fix typo * [DML EP] Pad operator: Handle negative pad counts (#11974) * Pad fallback to CPU * Added queryPad in operatorRegistration.cpp * Acknowledged PR comments * Used any_of * used none_of instead of any_of Co-authored-by: Sumit Agarwal <sumitagarwal@microsoft.com> * Add warning about future computation change for ConvTranspose with auto_pad (#11984) * Add warning about future computation change for Convtranspose with auto_pad * improve msg * update TODO to make lint happy * update more contents for warning and add if * valid was not infected * move it into kernel registration * parse auto_pad myself * try to use conv_transpose_attrs_.auto_pad directly * update roialign cuda impl to onnx opset16 (#12036) * roialign opset16 * fix * fix * Fix windows eager build break by pinning to torch version 1.11.0 (#12033) Fix windows and linux eager build to torch 1.11.0. * Skip Constant Folding for ops producing an optional type output (#11839) * Disable sequence-type tests since C# infra doesn't support well (#12037) * Extend lifetime of KernelDef when creating a standalone op (#12057) place tmp kernel def as local variable to cover the lifetime of kernel creation * Add targets files for new .net6 frameworks (#12016) * Add net6 targets. Remove maccatalyst as we don't have a native build targetting that. * Set platform in macos targets * Add targetFramework entries * Move NativeLib.DllName definition and set using preprocessor values for simplicity. Couldn't get it to build with the preprocessor based setup when it was in a separate file. Update the nuspec generation to set platform version for .net6 targets. TODO: Validate versions. I copied them from the managed nuget package the packaging pipeline generated prior to adding targets. Possibly w could/should lower some of the versions. Hopefully the need to specify a version goes away when the release version of VS2022 supports .net6. * Try android 31.1 as https://github.com/actions/virtual-environments/blob/main/images/win/Windows2022-Readme.md suggests that should be available on the CI machines * Fix patch version mismatch Add some extra debug info in case it helps * Debug nuget location in CI * Add workspace entry back in * Add steps * One more attempt with hardcoded nuget.exe path and original android31.0 version * Better fix - found explicit nuget download and updated version there. * flake8 fixes * Fix black complaints. * Exit Microsoft_ML_OnnxRuntime_CheckPrerequisites for net6 iOS. * Removed outdated comment * Fix DML custom operators which set descriptor heap to command list (#12059) * Make C# runtest.sh automatically set latest opset (#12039) * Update C# runtest.sh for opset 17 Should have been part of https://github.com/microsoft/onnxruntime/pull/11924 * get appropriate opset version from onnx doc * use absolute rather than relative path * fix typo in var name * Disable DML command list reuse for Xbox (#12063) disable cl reuse for xbox * Add data type check in ConvAddRelu fusion (#12058) * Add undocumented attribute to disable generation of Java bindings from the Android AAR. (#12075) The generated bindings causes C# build errors that require workaround code. Disabling generation should avoid the need for any workarounds. As the user has the C# ORT package with the C# to C bindings there's no need for binding generation that calls the ORT Java API (which is C# -> Java ->C). * enable the extensions custom build for java and android (#11823) * generate quantization parameter for outputs (#12089) * DML EP Update to DML 1.9 (#12090) * Update to DML 1.9 * Appease obnoxious Python formatting tool * Fix orttraining-linux-ci-pipeline - Symbolic shape infer (#11965) fix symbolic shape error due to upgraded numpy + legacy sympy * check consumers of dq node before swap dq and transpose (#12099) * check consumers of dq node before swap dq and transpose * add unit test Co-authored-by: Gary Miguel <garymiguel@microsoft.com> Co-authored-by: Preetha Veeramalai <preetha.veeramalai@intel.com> Co-authored-by: mayavijx <mayax.vijayan@intel.com> Co-authored-by: mohsin <mohsinx.mohammad@intel.com> Co-authored-by: Ye Wang <52801275+wangyems@users.noreply.github.com> Co-authored-by: Dmitri Smirnov <yuslepukhin@users.noreply.github.com> Co-authored-by: G. Ramalingam <grama@microsoft.com> Co-authored-by: Dwayne Robinson <dwayner@microsoft.com> Co-authored-by: Sheil Kumar <smk2007@gmail.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: sumitsays <sumitagarwal330@gmail.com> Co-authored-by: Sumit Agarwal <sumitagarwal@microsoft.com> Co-authored-by: Chun-Wei Chen <jacky82226@gmail.com> Co-authored-by: George Wu <jywu@microsoft.com> Co-authored-by: Wil Brady <25513670+WilBrady@users.noreply.github.com> Co-authored-by: Hariharan Seshadri <shariharan91@gmail.com> Co-authored-by: Wei-Sheng Chin <wschin@outlook.com> Co-authored-by: Scott McKay <skottmckay@gmail.com> Co-authored-by: Jeff Bloomfield <38966965+jeffbloo@users.noreply.github.com> Co-authored-by: Justin Stoecker <justoeck@microsoft.com> Co-authored-by: Wenbing Li <10278425+wenbingl@users.noreply.github.com> Co-authored-by: Yufeng Li <liyufeng1987@gmail.com> Co-authored-by: pengwa <pengwa@microsoft.com> |
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ONNX Runtime is a cross-platform inference and training machine-learning accelerator.
ONNX Runtime inference can enable faster customer experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc. ONNX Runtime is compatible with different hardware, drivers, and operating systems, and provides optimal performance by leveraging hardware accelerators where applicable alongside graph optimizations and transforms. Learn more →
ONNX Runtime training can accelerate the model training time on multi-node NVIDIA GPUs for transformer models with a one-line addition for existing PyTorch training scripts. Learn more →
Get Started
General Information: onnxruntime.ai
Usage documention and tutorials: onnxruntime.ai/docs
Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Build Pipeline Status
| System | CPU | GPU | EPs |
|---|---|---|---|
| Windows | |||
| Linux | |||
| Mac | |||
| Android | |||
| iOS | |||
| WebAssembly |
Data/Telemetry
Windows distributions of this project may collect usage data and send it to Microsoft to help improve our products and services. See the privacy statement for more details.
Contributions and Feedback
We welcome contributions! Please see the contribution guidelines.
For feature requests or bug reports, please file a GitHub Issue.
For general discussion or questions, please use GitHub Discussions.
Code of Conduct
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
License
This project is licensed under the MIT License.