42d62b8f2b
### Description Added changes to MIGraphX EP to suppoert stable diffusion 1. Added parameterized input dimensions to not trigger a precompile to set input parameters in the EP 2. Removed input checking for Resize operator in EP as MIGraphX already performs these checks 3. Add support to benchmark script to use the MIGraphX execution provider 4. Add support for an odd valued batch size (3) that was seen on other benchmarks we were performing comparison on. ### Motivation and Context These changes are required to get stable diffusion mdoels to run on MIGraphX through the EP. Without these changes we see the following incorrect behavior. 1. Resize operators are pushed onto the CPU EP instead of MIGraphX, causing a significant slowdown during runs 2. Precompile operations incorrectly parse input_ids parameter for our text model, with a 1, which breaks during MIGraphX Compile of onnx. This in turn throws an error and stops any setup before inference. 3. Selecting the correct EP in the benchmark script which was previously missing the MIGraphX option 5. Suppressed an error we keep seeing with pthread_set_affinity - this is a quality of life change when using the MIGraphX EP This was testing with the benchmark.py script using stable diffusion v2 located in onnxruntime/onnxruntime/python/tools/transformers/models/stable_diffusion/ --------- Co-authored-by: Ted Themistokleous <tthemist@amd.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 & 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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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.