d8dfda2e08
### Minor fix for differently scoped cpu_ep
cpu_ep is under `#ifndef DISABLE_CONTRIB_OPS`, but one of its usage is
not under the same condition.
```
#ifndef DISABLE_CONTRIB_OPS
const InlinedHashSet<std::string_view> cpu_ep = {onnxruntime::kCpuExecutionProvider};
#endif
```
### Motivation and Context
Postmoterm: https://github.com/microsoft/onnxruntime/pull/15461 passed
all CIs except Linux/Windows TVM CIs. I did not check the detailed error
message then because they are failed for some reason for a few days at
least. While checking the details, after PR 15461, the error messge
changes from
Before constant sharing change: TVM CI error message:
```
https://github.com/microsoft/onnxruntime/actions/runs/4700368634/jobs/8334955814
ERROR: testBooleanInputs (__main__.TestInferenceSession)
----------------------------------------------------------------------
Traceback (most recent call last):
File "onnxruntime_test_python.py", line 617, in testBooleanInputs
sess = onnxrt.InferenceSession(get_name("logicaland.onnx"), providers=available_providers)
File "D:\a\onnxruntime\onnxruntime\build\Release\Release\onnxruntime\capi\onnxruntime_inference_collection.py", line 383, in __init__
self._create_inference_session(providers, provider_options, disabled_optimizers)
File "D:\a\onnxruntime\onnxruntime\build\Release\Release\onnxruntime\capi\onnxruntime_inference_collection.py", line 435, in _create_inference_session
sess.initialize_session(providers, provider_options, disabled_optimizers)
onnxruntime.capi.onnxruntime_pybind11_state.RuntimeException: [ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : Exception during initialization: D:\a\onnxruntime\onnxruntime\onnxruntime\core\providers\tvm\tvm_api.cc:49 onnxruntime::tvm::TVMCompile compile != nullptr was false. Unable to retrieve 'tvm_onnx_import_and_compile'.
```
to
```
D:\a\onnxruntime\onnxruntime\onnxruntime\core\optimizer\graph_transformer_utils.cc(213,67): error C2065: 'cpu_ep': undeclared identifier [D:\a\onnxruntime\onnxruntime\build\Release\onnxruntime_optimizer.vcxproj]
D:\a\onnxruntime\onnxruntime\onnxruntime\core\optimizer\graph_transformer_utils.cc(213,19): error C2672:
```
This PR fixes the build the issue, The error message of Windows/Linux
TVM CIs are back to the original ones.
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| CONTRIBUTING.md | ||
| lgtm.yml | ||
| LICENSE | ||
| NuGet.config | ||
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| ORT_icon_for_light_bg.png | ||
| packages.config | ||
| pyproject.toml | ||
| README.md | ||
| requirements-dev.txt | ||
| requirements-doc.txt | ||
| requirements-training.txt | ||
| requirements.txt.in | ||
| SECURITY.md | ||
| setup.py | ||
| ThirdPartyNotices.txt | ||
| VERSION_NUMBER | ||
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.