saymrwulf/onnxruntime: ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

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ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

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raoanag c3fe4467a9 Enable QLinearAveragePooling DML EP (#17384 ) ### Description DML EP Implementation for [QLinearAveragePool](https://github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md#com.microsoft.QLinearAveragePool) ``` Note: Google Test filter = QLinearPool* [==========] Running 72 tests from 2 test suites. [----------] Global test environment set-up. [----------] 36 tests from QLinearGlobalAveragePool [ RUN ] QLinearGlobalAveragePool.Nhwc_1x1x32x32 [ OK ] QLinearGlobalAveragePool.Nhwc_1x1x32x32 (410 ms) [ RUN ] QLinearGlobalAveragePool.Nchw_1x32x32x1 [ OK ] QLinearGlobalAveragePool.Nchw_1x32x32x1 (641 ms) [ RUN ] QLinearGlobalAveragePool.Nhwc_1x256x8x8 [ OK ] QLinearGlobalAveragePool.Nhwc_1x256x8x8 (156 ms) [ RUN ] QLinearGlobalAveragePool.Nchw_1x8x8x256 [ OK ] QLinearGlobalAveragePool.Nchw_1x8x8x256 (134 ms) [ RUN ] QLinearGlobalAveragePool.Nhwc_1x255x7x7 [ OK ] QLinearGlobalAveragePool.Nhwc_1x255x7x7 (160 ms) [ RUN ] QLinearGlobalAveragePool.Nchw_1x7x7x255 [ OK ] QLinearGlobalAveragePool.Nchw_1x7x7x255 (145 ms) [ RUN ] QLinearGlobalAveragePool.Nhwc_1x255x8x8 [ OK ] QLinearGlobalAveragePool.Nhwc_1x255x8x8 (148 ms) [ RUN ] QLinearGlobalAveragePool.Nchw_1x8x8x255 [ OK ] QLinearGlobalAveragePool.Nchw_1x8x8x255 (129 ms) [ RUN ] QLinearGlobalAveragePool.Nhwc_1x256x7x7 [ OK ] QLinearGlobalAveragePool.Nhwc_1x256x7x7 (134 ms) [ RUN ] QLinearGlobalAveragePool.Nchw_1x7x7x256 [ OK ] QLinearGlobalAveragePool.Nchw_1x7x7x256 (131 ms) [ RUN ] QLinearGlobalAveragePool.Nhwc_3x256x8x8 [ OK ] QLinearGlobalAveragePool.Nhwc_3x256x8x8 (159 ms) [ RUN ] QLinearGlobalAveragePool.Nchw_3x8x8x256 [ OK ] QLinearGlobalAveragePool.Nchw_3x8x8x256 (168 ms) [ RUN ] QLinearGlobalAveragePool.Nhwc_3x255x7x7 [ OK ] QLinearGlobalAveragePool.Nhwc_3x255x7x7 (139 ms) [ RUN ] QLinearGlobalAveragePool.Nchw_3x7x7x255 [ OK ] QLinearGlobalAveragePool.Nchw_3x7x7x255 (170 ms) [ RUN ] QLinearGlobalAveragePool.Nhwc_3x255x8x8 [ OK ] QLinearGlobalAveragePool.Nhwc_3x255x8x8 (155 ms) [ RUN ] QLinearGlobalAveragePool.Nchw_3x8x8x255 [ OK ] QLinearGlobalAveragePool.Nchw_3x8x8x255 (156 ms) [ RUN ] QLinearGlobalAveragePool.Nhwc_3x256x7x7 [ OK ] QLinearGlobalAveragePool.Nhwc_3x256x7x7 (133 ms) [ RUN ] QLinearGlobalAveragePool.Nchw_3x7x7x256 [ OK ] QLinearGlobalAveragePool.Nchw_3x7x7x256 (149 ms) [ RUN ] QLinearGlobalAveragePool.Nhwc_1x1x32x32_S8 [ OK ] QLinearGlobalAveragePool.Nhwc_1x1x32x32_S8 (131 ms) [ RUN ] QLinearGlobalAveragePool.Nchw_1x32x32x1_S8 [ OK ] QLinearGlobalAveragePool.Nchw_1x32x32x1_S8 (127 ms) [ RUN ] QLinearGlobalAveragePool.Nhwc_1x256x8x8_S8 [ OK ] QLinearGlobalAveragePool.Nhwc_1x256x8x8_S8 (153 ms) [ RUN ] QLinearGlobalAveragePool.Nchw_1x8x8x256_S8 [ OK ] QLinearGlobalAveragePool.Nchw_1x8x8x256_S8 (129 ms) [ RUN ] QLinearGlobalAveragePool.Nhwc_1x255x7x7_S8 [ OK ] QLinearGlobalAveragePool.Nhwc_1x255x7x7_S8 (133 ms) [ RUN ] QLinearGlobalAveragePool.Nchw_1x7x7x255_S8 [ OK ] QLinearGlobalAveragePool.Nchw_1x7x7x255_S8 (135 ms) [ RUN ] QLinearGlobalAveragePool.Nhwc_1x255x8x8_S8 [ OK ] QLinearGlobalAveragePool.Nhwc_1x255x8x8_S8 (129 ms) [ RUN ] QLinearGlobalAveragePool.Nchw_1x8x8x255_S8 [ OK ] QLinearGlobalAveragePool.Nchw_1x8x8x255_S8 (152 ms) [ RUN ] QLinearGlobalAveragePool.Nhwc_1x256x7x7_S8 [ OK ] QLinearGlobalAveragePool.Nhwc_1x256x7x7_S8 (140 ms) [ RUN ] QLinearGlobalAveragePool.Nchw_1x7x7x256_S8 [ OK ] QLinearGlobalAveragePool.Nchw_1x7x7x256_S8 (133 ms) [ RUN ] QLinearGlobalAveragePool.Nhwc_3x256x8x8_S8 [ OK ] QLinearGlobalAveragePool.Nhwc_3x256x8x8_S8 (135 ms) [ RUN ] QLinearGlobalAveragePool.Nchw_3x8x8x256_S8 [ OK ] QLinearGlobalAveragePool.Nchw_3x8x8x256_S8 (147 ms) [ RUN ] QLinearGlobalAveragePool.Nhwc_3x255x7x7_S8 [ OK ] QLinearGlobalAveragePool.Nhwc_3x255x7x7_S8 (156 ms) [ RUN ] QLinearGlobalAveragePool.Nchw_3x7x7x255_S8 [ OK ] QLinearGlobalAveragePool.Nchw_3x7x7x255_S8 (155 ms) [ RUN ] QLinearGlobalAveragePool.Nhwc_3x255x8x8_S8 [ OK ] QLinearGlobalAveragePool.Nhwc_3x255x8x8_S8 (138 ms) [ RUN ] QLinearGlobalAveragePool.Nchw_3x8x8x255_S8 [ OK ] QLinearGlobalAveragePool.Nchw_3x8x8x255_S8 (155 ms) [ RUN ] QLinearGlobalAveragePool.Nhwc_3x256x7x7_S8 [ OK ] QLinearGlobalAveragePool.Nhwc_3x256x7x7_S8 (144 ms) [ RUN ] QLinearGlobalAveragePool.Nchw_3x7x7x256_S8 [ OK ] QLinearGlobalAveragePool.Nchw_3x7x7x256_S8 (139 ms) [----------] 36 tests from QLinearGlobalAveragePool (5968 ms total) [----------] 36 tests from QLinearPoolTest [ RUN ] QLinearPoolTest.AveragePool1D_ExcludePadPixel [ OK ] QLinearPoolTest.AveragePool1D_ExcludePadPixel (480 ms) [ RUN ] QLinearPoolTest.AveragePool1D_IncludePadPixel [ OK ] QLinearPoolTest.AveragePool1D_IncludePadPixel (481 ms) [ RUN ] QLinearPoolTest.AveragePool2D_ExcludePadPixel [ OK ] QLinearPoolTest.AveragePool2D_ExcludePadPixel (512 ms) [ RUN ] QLinearPoolTest.AveragePool2D_IncludePadPixel [ OK ] QLinearPoolTest.AveragePool2D_IncludePadPixel (455 ms) [ RUN ] QLinearPoolTest.AveragePool2D_MultiChannel [ OK ] QLinearPoolTest.AveragePool2D_MultiChannel (463 ms) [ RUN ] QLinearPoolTest.AveragePool3D_ExcludePadPixel [ OK ] QLinearPoolTest.AveragePool3D_ExcludePadPixel (448 ms) [ RUN ] QLinearPoolTest.AveragePool3D_IncludePadPixel [ OK ] QLinearPoolTest.AveragePool3D_IncludePadPixel (458 ms) [ RUN ] QLinearPoolTest.AveragePool1D_ExcludePadPixel_nhwc [ OK ] QLinearPoolTest.AveragePool1D_ExcludePadPixel_nhwc (171 ms) [ RUN ] QLinearPoolTest.AveragePool1D_IncludePadPixel_nhwc [ OK ] QLinearPoolTest.AveragePool1D_IncludePadPixel_nhwc (169 ms) [ RUN ] QLinearPoolTest.AveragePool2D_ExcludePadPixel_nhwc [ OK ] QLinearPoolTest.AveragePool2D_ExcludePadPixel_nhwc (152 ms) [ RUN ] QLinearPoolTest.AveragePool2D_IncludePadPixel_nhwc [ OK ] QLinearPoolTest.AveragePool2D_IncludePadPixel_nhwc (660 ms) [ RUN ] QLinearPoolTest.AveragePool2D_MultiChannel_nhwc [ OK ] QLinearPoolTest.AveragePool2D_MultiChannel_nhwc (150 ms) [ RUN ] QLinearPoolTest.AveragePool3D_ExcludePadPixel_nhwc [ OK ] QLinearPoolTest.AveragePool3D_ExcludePadPixel_nhwc (145 ms) [ RUN ] QLinearPoolTest.AveragePool3D_IncludePadPixel_nhwc [ OK ] QLinearPoolTest.AveragePool3D_IncludePadPixel_nhwc (146 ms) [ RUN ] QLinearPoolTest.AveragePool2D_BigImage [ OK ] QLinearPoolTest.AveragePool2D_BigImage (505 ms) [ RUN ] QLinearPoolTest.AveragePool2D_BigImage_nhwc [ OK ] QLinearPoolTest.AveragePool2D_BigImage_nhwc (161 ms) [ RUN ] QLinearPoolTest.AveragePool2D_Global [ OK ] QLinearPoolTest.AveragePool2D_Global (481 ms) [ RUN ] QLinearPoolTest.AveragePool2D_Global_nhwc [ OK ] QLinearPoolTest.AveragePool2D_Global_nhwc (152 ms) [ RUN ] QLinearPoolTest.AveragePool1D_ExcludePadPixel_S8 [ OK ] QLinearPoolTest.AveragePool1D_ExcludePadPixel_S8 (461 ms) [ RUN ] QLinearPoolTest.AveragePool1D_IncludePadPixel_S8 [ OK ] QLinearPoolTest.AveragePool1D_IncludePadPixel_S8 (448 ms) [ RUN ] QLinearPoolTest.AveragePool2D_ExcludePadPixel_S8 [ OK ] QLinearPoolTest.AveragePool2D_ExcludePadPixel_S8 (471 ms) [ RUN ] QLinearPoolTest.AveragePool2D_IncludePadPixel_S8 [ OK ] QLinearPoolTest.AveragePool2D_IncludePadPixel_S8 (473 ms) [ RUN ] QLinearPoolTest.AveragePool2D_MultiChannel_S8 [ OK ] QLinearPoolTest.AveragePool2D_MultiChannel_S8 (1507 ms) [ RUN ] QLinearPoolTest.AveragePool3D_ExcludePadPixel_S8 [ OK ] QLinearPoolTest.AveragePool3D_ExcludePadPixel_S8 (477 ms) [ RUN ] QLinearPoolTest.AveragePool3D_IncludePadPixel_S8 [ OK ] QLinearPoolTest.AveragePool3D_IncludePadPixel_S8 (493 ms) [ RUN ] QLinearPoolTest.AveragePool1D_ExcludePadPixel_nhwc_S8 [ OK ] QLinearPoolTest.AveragePool1D_ExcludePadPixel_nhwc_S8 (158 ms) [ RUN ] QLinearPoolTest.AveragePool1D_IncludePadPixel_nhwc_S8 [ OK ] QLinearPoolTest.AveragePool1D_IncludePadPixel_nhwc_S8 (146 ms) [ RUN ] QLinearPoolTest.AveragePool2D_ExcludePadPixel_nhwc_S8 [ OK ] QLinearPoolTest.AveragePool2D_ExcludePadPixel_nhwc_S8 (146 ms) [ RUN ] QLinearPoolTest.AveragePool2D_IncludePadPixel_nhwc_S8 [ OK ] QLinearPoolTest.AveragePool2D_IncludePadPixel_nhwc_S8 (158 ms) [ RUN ] QLinearPoolTest.AveragePool2D_MultiChannel_nhwc_S8 [ OK ] QLinearPoolTest.AveragePool2D_MultiChannel_nhwc_S8 (157 ms) [ RUN ] QLinearPoolTest.AveragePool3D_ExcludePadPixel_nhwc_S8 [ OK ] QLinearPoolTest.AveragePool3D_ExcludePadPixel_nhwc_S8 (145 ms) [ RUN ] QLinearPoolTest.AveragePool3D_IncludePadPixel_nhwc_S8 [ OK ] QLinearPoolTest.AveragePool3D_IncludePadPixel_nhwc_S8 (147 ms) [ RUN ] QLinearPoolTest.AveragePool2D_BigImage_S8 [ OK ] QLinearPoolTest.AveragePool2D_BigImage_S8 (537 ms) [ RUN ] QLinearPoolTest.AveragePool2D_BigImage_nhwc_S8 [ OK ] QLinearPoolTest.AveragePool2D_BigImage_nhwc_S8 (173 ms) [ RUN ] QLinearPoolTest.AveragePool2D_Global_S8 [ OK ] QLinearPoolTest.AveragePool2D_Global_S8 (457 ms) [ RUN ] QLinearPoolTest.AveragePool2D_Global_nhwc_S8 [ OK ] QLinearPoolTest.AveragePool2D_Global_nhwc_S8 (150 ms) [----------] 36 tests from QLinearPoolTest (12914 ms total) [----------] Global test environment tear-down [==========] 72 tests from 2 test suites ran. (18885 ms total) [ PASSED ] 72 tests. memleakdbg: ----- No memory leaks detected ----- ```		2023-09-21 19:21:07 -07:00
.config
.devcontainer
.gdn	Update win-ci-pipeline.yml: enable xnnpack tests (#16244 )	2023-06-14 19:12:42 -07:00
.github	Bump actions/deploy-pages from 1 to 2 (#16402 )	2023-07-24 16:13:59 -07:00
.pipelines	Workaround to upgrade VS2022 for Windows ARM build (#16826 )	2023-07-25 08:35:52 +08:00
.vscode
cgmanifests	[TensorRT EP] TRT 8.6 minor version update (#16475 )	2023-06-26 10:44:27 -07:00
cmake	Workaround to upgrade VS2022 for Windows ARM build (#16826 )	2023-07-25 08:35:52 +08:00
csharp	Change DML GPU pool in Windows GPU workflow use Visual Studio 2022 (#16784 )	2023-07-23 10:07:21 +08:00
dockerfiles	Enable model subgraph execution in OVEP and setting the OpenVINO dll's to the path from the OpenVINO pypi packge in OVEP and fix OVEP windows io buffer sample (#16147 )	2023-06-16 19:47:09 -07:00
docs	[Better Engineering] Bump ruff to 0.0.278 and fix new lint errors (#16789 )	2023-07-21 12:53:41 -07:00
include/onnxruntime/core	Fix bug with saving model optimized by inference session (#16716 )	2023-07-20 18:44:28 -07:00
java	[java] Fp16 fix for android/react native (#16832 )	2023-07-25 12:31:32 -07:00
js	Bump word-wrap from 1.2.3 to 1.2.4 in /js/react_native (#16755 )	2023-07-22 13:36:49 -07:00
objectivec	Objective-C Add Support to Create and Query String ORTValues (#16764 )	2023-07-20 17:39:29 -07:00
onnxruntime	Enable QLinearAveragePooling DML EP (#17384 )	2023-09-21 19:21:07 -07:00
orttraining	[DORT] Enable Dynamic Shape in DORT and Use Different InferenceSession's when Inputs Are Not Compatible (#16753 )	2023-07-24 16:54:01 -07:00
rust
samples	Enable pylint and numpy rules (#15218 )	2023-03-27 20:37:53 -07:00
swift/OnnxRuntimeBindingsTests	Add iOS Swift Package Manager support (#15297 )	2023-04-20 16:18:35 +10:00
tools	Update upload_pod_archive_and_update_podspec.sh to take path pattern (#16810 )	2023-07-25 08:55:31 -07:00
winml	Fix DML regression from allocator refactor and enable unrounded weight allocations through ORT API	2023-08-06 09:49:39 -07:00
.clang-format	Run clang-format in CI (#15524 )	2023-04-18 09:26:58 -07:00
.clang-tidy
.dockerignore
.gitattributes
.gitignore	remove 'lib/' from .gitignore (#15613 )	2023-04-24 18:43:32 -07:00
.gitmodules	Update eigen to 3.4 and remove the eigen from git submodule (#15875 )	2023-05-11 11:56:59 -07:00
.lintrunner.toml	Minimal Build for On-Device Training (#16326 )	2023-06-22 12:27:23 -07:00
build.bat	Upgrade old Python version in packaging pipeline (#16667 )	2023-07-17 08:24:47 -07:00
build.sh	Upgrade old Python version in packaging pipeline (#16667 )	2023-07-17 08:24:47 -07:00
CITATION.cff
CODEOWNERS	Add owners for public facing API files (#15288 )	2023-03-30 17:16:15 -07:00
CONTRIBUTING.md
lgtm.yml
LICENSE
NuGet.config
ort.wprp
ORT_icon_for_light_bg.png
Package.swift	Objective-C Add Support to Create and Query String ORTValues (#16764 )	2023-07-20 17:39:29 -07:00
packages.config	[DML EP] Update DirectML version to 1.12.0 (#16011 )	2023-05-18 19:37:12 -07:00
pyproject.toml	[Better Engineering] Bump ruff to 0.0.278 and fix new lint errors (#16789 )	2023-07-21 12:53:41 -07:00
README.md	add third-party pipeline status to README.md (#16155 )	2023-05-31 22:14:39 -07:00
requirements-dev.txt	Remove codecov from requirements-dev.txt (#15487 )	2023-04-12 18:48:02 -07:00
requirements-doc.txt
requirements-lintrunner.txt	[Better Engineering] Bump ruff to 0.0.278 and fix new lint errors (#16789 )	2023-07-21 12:53:41 -07:00
requirements-training.txt
requirements.txt.in
SECURITY.md
setup.py	Triton Codegen for ORTModule (#15831 )	2023-07-13 18:17:58 +08:00
ThirdPartyNotices.txt	Implement openAI endpoint invoker for nuget (#15797 )	2023-05-11 22:04:02 -07:00
VERSION_NUMBER	Update VERSION_NUMBER (#15773 )	2023-05-03 15:07:34 -07:00

README.md

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 & Resources

General Information: onnxruntime.ai
Usage documention and tutorials: onnxruntime.ai/docs
YouTube video tutorials: youtube.com/@ONNXRuntime
Upcoming Release Roadmap
Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples

Builtin Pipeline Status

System	Inference	Training
Windows
Linux
Mac
Android
iOS
Web
Other

Third-party Pipeline Status

System	Inference	Training
Linux

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.