559bd52252
### Description - Update QNN CI Pipelines to use QNN SDK version 2.17.0 - **Print warning if unit test requires adjusted tolerance to pass** - **Temporarily disable unloading QnnCpu.dll for windows x64 due to crash when calling FreeLibrary** - Enable fixed HTP tests - QnnHTPBackendTests.LayerNorm1D_LastAxis_DynamicScale - QnnHTPBackendTests.GlobalMaxPool_LargeInput2_u8 - QnnHTPBackendTests.ReduceSumS8Opset13_Rank5 - QnnHTPBackendTests.ReduceSumU8Opset13_Rank5_LastAxis - QnnHTPBackendTests.WhereLargeDataBroadcastU8 - QnnHTPBackendTests.WhereLargeDataBroadcastTransformedU8 - Enabled fixed CPU tests - QnnCPUBackendTests.Resize_DownSample_Linear_AlignCorners_scales - Increased tolerance for HTP tests that are less accurate on QNN SDK 2.17.0 - QnnHTPBackendTests.AveragePool_CountIncludePad_HTP_u8 - QnnHTPBackendTests.AveragePool_AutopadSameUpper_HTP_u8 - QnnHTPBackendTests.AveragePool_AutopadSameLower_HTP_u8 - QnnHTPBackendTests.ConvU8U8S32_bias_dynamic_input - QnnHTPBackendTests.ConvU8U8S32_bias_initializer - QnnHTPBackendTests.ConvU8U8S32_large_input1_padding_bias_initializer - QnnHTPBackendTests.LRNSize3 - QnnHTPBackendTests.LRNSize5 - QnnHTPBackendTests.MaxPool_Large_Input_HTP_u8 - QnnHTPBackendTests.MaxPool_LargeInput_1Pads - QnnHTPBackendTests.Resize_DownSample_Linear_HalfPixel - QnnHTPBackendTests.ResizeU8_2xLinearPytorchHalfPixel - QnnHTPBackendTests.ResizeU8_2xLinearHalfPixel - QnnHTPBackendTests.ResizeU8_2xLinearAlignCorners - QnnHTPBackendTests.ResizeU8_2xLinearAsymmetric - Disabled ONNX model tests - averagepool_2d_ceil: Accuracy issues **only on Windows x64 QnnCpu.dll** - Disabled QDQ model tests (onnx_test_runner) - facedetection_op8_qdq: Accuracy issues - Disabled CPU EP tests (these use QnnCpu.dll) - ActivationOpTest.Relu: QNN SDK 2.17 Relu treats inf as FLT_MAX - GemmOpTypedTests/0.TestGemmBroadcast: Inaccuracy when weight is initializer and bias is not - MathOpTest.MatMulFloatType "test padding and broadcast B > A": Inaccuracy (**only linux**) - Fix Gemm translation bugs in QNN EP: - Do not skip processing of inputs that need to be transposed. ### Motivation and Context - Allow testing with newest QNN SDK version - Take advantage of improvements to enable new models. |
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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 & Resources
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General Information: onnxruntime.ai
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Usage documention and tutorials: onnxruntime.ai/docs
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YouTube video tutorials: youtube.com/@ONNXRuntime
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Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Builtin Pipeline Status
| System | Inference | Training |
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| 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.