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### Description Background: User save large model with initializer data in external file. e.g: onnx.save_model(onnx_model, "path/to/save/the/model.onnx", save_as_external_data=True, all_tensors_to_one_file=True, location="filename", size_threshold=1024). In that case, Ort loads the model, get the external initializer information (external file name, offset, length) and use the model path to find the external file, and locate to the tensor data via the offset and length. But it won't work if user load the model from memory, since Ort lost track of the model path. This PR adds API/session option to let user provide a table with external initializer file name as the key, the pointer to the loaded external file in memory and the buffer length as value. So that 1. user can load the model from memory buffer with external initializers in memory buffer too. 2. the initializers can be shared across sessions, for different EPs. 3. user can load the file in any way they want, e.g mmap. Internally, 1. at session creation time, Ort goes through the external initializers in the graph, gets the file name, offset, data length of the external initializers from Tensorproto . 2. With the file name, Ort get the file in memory buffer and buffer length from the table user provided. 4. Ort locates the tensor buffer from file in memory buffer (user provided) using the offset and data length (from Tensorproto ). 5. Ort creates the Tensor and replace the existing Tensor in the graph. ### Motivation and Context https://github.com/onnx/onnx/blob/main/docs/ExternalData.md For a model with external data, the Tensorproto may have initializer data in a separate file. The external file location is set via the file path relative to the model path. With the API to load model from memory buffer, it lost track of the model path. So it causes error if the model has external data. By adding a session option to set the external data buffer, Ort can find the external data correctly if model loaded from memory buffer. |
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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 documentation 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
Builtin Pipeline Status
| System | Inference | Training |
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| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
Third-party Pipeline Status
| System | Inference | Training |
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| Linux |  |
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.