7bec80d92a
### Fix reference count for autograd
When PythonOp kernel initialized, `AddPointerScalarArgs` creates
`const_args_` which put all non-tensor references (including
ProcessGroup, string, or other user types) in it.
In kernel's destructor, all ref cnt got decreased for `const_args_`.
```
void PythonOpBase::Clear() {
for (auto ptr : const_args_) {
auto obj = reinterpret_cast<PyObject*>(ptr);
Py_DECREF(obj);
}
}
```
It means, we did not increase cnt, but just decrease cnt. Running the
unit, segmentation fault will be thrown. The simple fix is to remove the
Py_DECREF for those pointer-type constant inputs triggered by kernel
destructor.
NONTENSOR_OBJECT_POINTER_STORE is the place we increase the reference
during export, then the reference will remain until the python program
terminates.
Additionally tunings:
1. Move some logs into verbose instead of warning in case of flooding
training logs.
2. Move pointer type ref holding from python side
(NONTENSOR_OBJECT_POINTER_STORE) to
orttraining/orttraining/core/framework/torch/custom_function_register.h.
Then we use a consistent approach to manage all PythonOp related python
object/methonds ref count increasing and decreasing.
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| LICENSE | ||
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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
-
General Information: onnxruntime.ai
-
Usage documention and tutorials: onnxruntime.ai/docs
-
YouTube video tutorials: youtube.com/@ONNXRuntime
-
Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Build Pipeline Status
| System | Inference | Training |
|---|---|---|
| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
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.