2efb75bfe9
### Fold shape related operation at best efforts. This is a follow up for PR https://github.com/microsoft/onnxruntime/pull/12561. Create a specialized shape_optimzer to constant fold shape related operation. ShapeOptimizer at the best efforts to constant fold the dim values that exists from shape inferencing. This is helpful to simplify the graph, which on the other hand, help other graph transformers to do more. Transformer that traverses the graph top-down and performs shape optimizations. Try the best effort to constant fold the shape related to Shape node outputs: 1. Shape generates 1D tensor [12, 128, 512] (all dimensions have concrete dim value), which can be constant folded to an initializer including 1D tensor values [12, 128, 512]. (Some logic of ConstantFolding also does the same thing.) 2. Shape generate 1D tensor [batch_size, 128, 512] -> Slice(start=1,end=3), we can constant fold the Shape->Slice to an initializer including 1D tensor values [128, 512]. 3. Shape generate 1D tensor [batch_size, 128, 512] -> Gather(axes=[0], index=[2]), we can constant fold the Shape->Gather to an initializer including 1D tensor values [512]. 4. Shape 15 takes input of shape [batch_size, 128, 512], slicing from 1 to 2(exclusive), we can constant fold the Shape15(start=1,end=2) to an initializer including 1D tensor values [128]. This would help clean up the graph, combined with ConstantFolding, the graph would be much more simplified. ### Motivation and Context One direct motivation to have this is, we have a model subgraph like this:  The subgraph in the green rectangle is trying to get the value `30522`, with the changes in this PR, the subgraph will be constant folded. Plus ConstantFolding optimizer will further to optimize out the subsquent `Squeeze`/`Unsqueeze`/`ConcatTraining`, then we will have a clean very clean Reshape node, with its shape input be an constant `[-1, 20522]`. Having this simplified graph, our other compute optimizer can help further optimize the graph by re-ordering gather/reshape nodes. |
||
|---|---|---|
| .config | ||
| .devcontainer | ||
| .gdn | ||
| .github | ||
| .pipelines | ||
| .vscode | ||
| cgmanifests | ||
| cmake | ||
| csharp | ||
| dockerfiles | ||
| docs | ||
| include/onnxruntime/core | ||
| java | ||
| js | ||
| objectivec | ||
| onnxruntime | ||
| orttraining | ||
| package/rpm | ||
| rust | ||
| samples | ||
| swift/OnnxRuntimeBindingsTests | ||
| tools | ||
| winml | ||
| .clang-format | ||
| .clang-tidy | ||
| .dockerignore | ||
| .gitattributes | ||
| .gitignore | ||
| .gitmodules | ||
| .lintrunner.toml | ||
| build.amd64.1411.bat | ||
| build.bat | ||
| build.sh | ||
| CITATION.cff | ||
| CODEOWNERS | ||
| CONTRIBUTING.md | ||
| lgtm.yml | ||
| LICENSE | ||
| NuGet.config | ||
| ort.wprp | ||
| ORT_icon_for_light_bg.png | ||
| Package.swift | ||
| packages.config | ||
| pyproject.toml | ||
| README.md | ||
| requirements-dev.txt | ||
| requirements-doc.txt | ||
| requirements-lintrunner.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.