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* fix boost download url (#7843) * Topo sort the model before saving (#7913) * checkin toposort * review comments * revert and add TODO * Add shape inference to custom symbolic functions (#7937) **Description**: As title. **Motivation and Context** - PyTorch ONNX exporter heavily depends on ONNX shape inference to export accurate and efficient model. Custom symbolic function exports the op as contrib ops, thus exporter is unable to perform standard onnx shape inference. Models with dynamic shape inputs are affected. * Fix missing files on linux (#8066) * [Mobile package] Update required operator config with additional ops for wav2vec2. (#8079) Add some additional ops to the mobile package that are needed for the wav2vec2 model. * Add module attribute to ORTModule to support HuggingFace Trainer save_model (#8088) * Fix input schema extrator for ORTModule (#8098) * Fix 32bit Android java API crash (#8122) * Fix 32bit Android java API crash * fix code formating * [Mobile package] Update required operator config with additional ops for newer version of Wav2Vec 2. (#8123) This is an update to https://github.com/microsoft/onnxruntime/pull/8079 The sample application motivating the original update changed to use an updated version of the model. Now, fewer ops are required. This change removes the previously added ops which are no longer needed. * Add int64 as a required type to ConstantOfShape as it's used by the pytorch converter for Pad. (#8128) It's also used pointlessly for torch.tensor.repeat (although that usage should always be able to be constant folded). * Update logic in props.xml to account for shared provider library changes (#8138) * Ortmodule override torch.manual_seed() (#8131) * Ortmodule override torch.manual_seed() * Fix Python Cuda loading issues (#7939) * Fix mac shared_provider warning (#8153) Co-authored-by: Guoyu Wang <62914304+gwang-msft@users.noreply.github.com> Co-authored-by: Ye Wang <52801275+wangyems@users.noreply.github.com> Co-authored-by: Bowen Bao <bowbao@microsoft.com> Co-authored-by: Ryan Hill <38674843+RyanUnderhill@users.noreply.github.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: baijumeswani <bmeswani@microsoft.com> Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com> Co-authored-by: Scott McKay <skottmckay@gmail.com> Co-authored-by: Hariharan Seshadri <shariharan91@gmail.com> Co-authored-by: Sherlock <baihan.huang@gmail.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 →
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License
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