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### Description Consolidate the gpu data transfer in CUDA, ROCm and Migraphx EP. (1) Remove some redundant stream synchronize on default stream according to spec of cudaMemcpy (2) consolidate CUDA, ROCm and MigrphaX to try use same logic. ### Motivation This is a follow up on reviewing https://github.com/microsoft/onnxruntime/pull/22589. ### Context https://docs.nvidia.com/cuda/cuda-runtime-api/api-sync-behavior.html#api-sync-behavior ##### cudaMemcpy() * For transfers from pageable host memory to device memory, a stream sync is performed before the copy is initiated. The function will return once the pageable buffer has been copied to the staging memory for DMA transfer to device memory, **but the DMA to final destination may not have completed**. * For transfers from pinned host memory to device memory, the function is synchronous with respect to the host. * For transfers from device to either pageable or pinned host memory, the function returns only once the copy has completed. * For transfers from device memory to device memory, **no host-side synchronization is performed**. * For transfers from any host memory to any host memory, the function is fully synchronous with respect to the host. #### cudaMemcpyAsync * For transfers between device memory and pageable host memory, the function might be synchronous with respect to host. * For transfers from any host memory to any host memory, the function is fully synchronous with respect to the host. * If pageable memory must first be staged to pinned memory, the driver may synchronize with the stream and stage the copy into pinned memory. * For all other transfers, the function should be fully asynchronous. https://rocm.docs.amd.com/projects/HIP/en/latest/doxygen/html/group___memory.html ##### hipMemcpyAsync() If host or dest are not pinned, the memory copy will be performed synchronously. For best performance, use hipHostMalloc to allocate host memory that is transferred asynchronously. on HCC hipMemcpyAsync does not support overlapped H2D and D2H copies. For hipMemcpy, the copy is always performed by the device associated with the specified stream. ##### hipMemcpy() For hipMemcpy, the copy is always performed by the current device (set by hipSetDevice). https://github.com/ROCm/ROCm/blob/roc-5.7.x/tools/autotag/templates/rocm_changes/5.6.1.md ROCm 5.6.1 release note: hipMemcpy device-to-device (intra device) is now asynchronous with respect to the host |
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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 |
This project is tested with BrowserStack.
Third-party Pipeline Status
| System | Inference | Training |
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| Linux |  |
Releases
The current release and past releases can be found here: https://github.com/microsoft/onnxruntime/releases.
For details on the upcoming release, including release dates, announcements, features, and guidance on submitting feature requests, please visit the release roadmap: https://onnxruntime.ai/roadmap.
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.