1c38ceda49
* Op fusion support added
In addition the following op fusions are detected
- ConvRelu
- MatMulAdd
This change includes
- Change abstraction of Subgraph + node + tensor to support delete insert
modify
- add nodearg class to establish connection from tensor to node
- add graphtransformer class to support fusion
- add topological sort to ensure propoer node ordering after fusion
- add convrelu + matmuladd primitive to support execution of fused nodes
- Fix FusionResolution with missing tensors
when fusing, if the target node contains fewer tensors then original
patterns (Gelu and FastGelu ignores many initializers), potentially delete them
also from inputs and initializers
Also check tensor has no producer and consumer before deleting
Signed-off-by: Wang <zhaoyang.wang@intel.com>
* Gelu and FastGelu Fusion for DNNL EP
The basics of the Gelu/FastGelu code is modeled after:
- core/optimizer/fast_gelu_fusion.cc and
- core/optimizer/gelu_fusion.cc
OneDNN does not have support for 'Erf' unless it is part of 'Gelu'.
This results in detecting 'Gelu' fusion twice. Once when detecting
if the 'Erf' Operator is supported and again in the subgraph transformer
code. The capability code is finding the Gelu using onnxruntime:GraphViewer
and onnxruntime::Node. While the transformer code is using DnnlSubgraph
and DnnlNode. This results in two parts of code looking for the same
pattern but unfortanatly having little code reuse.
This also adds support for Biased versions of Gelu and FastGelu if they already
exist in a model.
Signed-off-by: George Nash <george.nash@intel.com>
* Code Clean Up
Signed-off-by: Wang <zhaoyang.wang@intel.com>
Co-authored-by: Wang <zhaoyang.wang@intel.com>
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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
General Information: onnxruntime.ai
Usage documention and tutorials: onnxruntime.ai/docs
Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Build Pipeline Status
| System | CPU | GPU | EPs |
|---|---|---|---|
| Windows | |||
| Linux | |||
| Mac | |||
| Android | |||
| iOS | |||
| WebAssembly |
Data/Telemetry
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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.