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### Description This change fixes the DLL delay load problem for the WebGPU EP and DirectML EP. See detailed explanation below. ### Problem When onnxruntime.dll uses delay loading for its dependencies, the dependencies are loaded using `LoadLibraryEx()`, which search the directory of process (.exe) instead of this library (onnxruntime.dll). This is a problem for usages of Node.js binding and python binding, because Windows will try to find the dependencies in the directory of node.exe or python.exe, which is not the directory of onnxruntime.dll. There was previous attempt to fix this by loading DirectML.dll in the initialization of onnxruntime nodejs binding, which works for DML EP but is not a good solution because it does not really "delay" the load. For WebGPU, the situation became worse because webgpu_dawn.dll depends on dxil.dll and dxcompiler.dll, which are explicitly dynamically loaded in the code using `LoadLibraryA()`. This has the same problem of the DLL search. ### Solutions For onnxruntime.dll loading its direct dependencies, it can be resolved by set the [`__pfnDliNotifyHook2` hook](https://learn.microsoft.com/en-us/cpp/build/reference/understanding-the-helper-function?view=msvc-170#structure-and-constant-definitions) to load from an absolute path that constructed from the onnxruntime.dll folder and the DLL name. For webgpu_dawn.dll loading dxil.dll and dxcompiler.dll, since they are explicitly loaded in the code, the hook does not work. Instead, it can be resolved by ~~using WIN32 API `SetDllDirectory()` to add the onnxruntime.dll folder to the search path.~~ preloading the 2 DLLs from the onnxruntime.dll folder . |
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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 documentation 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 |
|---|---|---|
| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
This project is tested with BrowserStack.
Third-party Pipeline Status
| System | Inference | Training |
|---|---|---|
| Linux |  |
Releases
The current release and past releases can be found here: https://github.com/microsoft/onnxruntime/releases.
For details on the upcoming release, including release dates, announcements, features, and guidance on submitting feature requests, please visit the release roadmap: https://onnxruntime.ai/roadmap.
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.