262b6bd3b7
### Description
The dml_provider_factory header file can't be used in C programs as it
defines C++ inline operators. This PR rearranges that header file so
that it looks like valid C when used from C, and also makes a couple of
small modifications to the Java code so it correctly binds to the DML EP
at build time.
I'm having some difficulty testing it as I think it's pulling in the old
version of DirectML on my computer and I can't figure out what the
library loading path is in Java to make it look at the recent version I
downloaded. So the test I added fails with:
```
InferenceTest > testDirectML() FAILED
ai.onnxruntime.OrtException: Error code - ORT_RUNTIME_EXCEPTION - message: Exception during initialization: <path-to-ort>\onnxruntime\core\providers\dml\DmlExecutionProvider\src\AbiCustomRegistry.cpp(518)\onnxruntime.dll!00007FFF74819333: (caller: 00007FFF74793509) Exception(3) tid(4f58) 80070057 The parameter is incorrect.
at app//ai.onnxruntime.OrtSession.createSession(Native Method)
at app//ai.onnxruntime.OrtSession.<init>(OrtSession.java:74)
at app//ai.onnxruntime.OrtEnvironment.createSession(OrtEnvironment.java:236)
at app//ai.onnxruntime.OrtEnvironment.createSession(OrtEnvironment.java:221)
at app//ai.onnxruntime.InferenceTest.openSessionSqueezeNet(InferenceTest.java:1961)
at app//ai.onnxruntime.InferenceTest.runProvider(InferenceTest.java:665)
at app//ai.onnxruntime.InferenceTest.testDirectML(InferenceTest.java:657)
```
But it does correctly compile, and this error seems very similar to
other issues with the DML provider when it doesn't like a model due to
the loaded library being old. The test is using the squeezenet file
that's been in the repo since 2019. If someone can help me figure out
how to get the right version of DML in the library path I can test it
more on my end. I tried adding the folder with the new version into the
system path, but I'm not very familiar with Windows' library loading
behaviour.
### Motivation and Context
Fixes #19656 to allow use of the DirectML EP from ORT Java.
cc @martinb35
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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
-
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 |
Third-party Pipeline Status
| System | Inference | Training |
|---|---|---|
| Linux |  |
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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.