0fa00429d5
### Description <!-- Describe your changes. --> 1. added script for t5 encoder self attention and t5 decoder self/cross attention fusions. 2. added simplified layernorm fusion for --external_data_format senario. (otherwise relying on ORT optimizer) 3. added rel_pos_bias shape inference code, modified attention/mha shape inference script. 4. reworked graph_topologic_sort() because the currently implementation is not functioning correctly. also added an option to topo-sort the graph in a deterministic way to let tests pass. note: 1. the t5-beamsearch export code is slightly modified. specifically, encoder_hidden_states(ehs) is no longer an input to the t5 decoder since the ehs is not actually used in the graph execution. 2. recent PRs do not add optimizations to t5 on cpu. 3. the fp32 model(encoder and decoder) for t5-small, t5-base and t5-large can get a parity of e-5 and the corresponding beam search models generate same results as pytorch. 4. fp16(mixed-precision) models, however, get a parity around 3e-2 and some has maximum diff a bit over 3e-2. But the beam search models still generate same results as pytorch (based on limited input data) 5. mt-5 model has a parity issue at the moment, even before any optimization. will investigate later. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> --------- Co-authored-by: Ubuntu <wy@v100-2.0cdb2e52twzevn1i4fi45bylyg.jx.internal.cloudapp.net> |
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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
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Usage documention 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
Build Pipeline Status
| System | Inference | Training |
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| Windows | ||
| Linux | ||
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| Web | ||
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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.