mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-07-03 03:58:54 +00:00
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
b87e8edb98
### Description model: phi-3-mini-4k-instruct avx2 symmetric blklen|updated prompt tps | baseline prompt tps | prompt tps change%|updated token gen tps | baseline token gen tps | token gen change% -|-|-|-|-|-|- 16 |49.5|70.0|-29.2%|9.6|10.8|-34.2% 32 |76.8|52.4|9.7%|15.2|14.6|4.1% 64 |78.2|71.4|9.5%|16.6|16.3|1.8% 128 |72.9|70.6|3.2%|17.1|16.8|1.7% 256 |83.7|63.6|31.6%|18.1|17.4|4% avx2 asymmetric blklen|updated prompt tps | baseline prompt tps | prompt tps change%|updated token gen tps | baseline token gen tps | token gen change% -|-|-|-|-|-|- 16 |50.7|61.5|-17.5%|9.6|9.2|4.3% 32 |77.4|52.4|47.7%|14.6|13.9|5.0% 64 |78.7|63.0|24.9%|16.2|15.9|1.8% 128 |80.0|61.9|29.2%|17.2|16.9|1.7% 256 |81.5|63.3|28.7%|17.9|17.3|3.4% avx2vnni symmetric blklen|updated prompt tps | baseline prompt tps | prompt tps change%|updated token gen tps | baseline token gen tps | token gen change% -|-|-|-|-|-|- 16 |82.9|117.0|-29.0%|15.9|19.3|-17.6% 32 |133.0|100.4|32.4%|26.1|24.5|6.5% 64 |166.9|118.8|40.4%|28.3|27.1|4.4% 128 |165.9|119.6|38.7%|29.3|28.5|2.8% 256 |165.2|119.6|38.1%|30.2|29.0|4.1% avx2vnni asymmetric blklen|updated prompt tps | baseline prompt tps | prompt tps change%|updated token gen tps | baseline token gen tps | token gen change% -|-|-|-|-|-|- 16 |80.2|118.9|-32.5%|15.1|16.7|-9.5% 32 |130.7|99.7|31.0%|25.0|23.8|5.0% 64 |168.7|124.9|35.0%|27.3|26.8|1.8% 128 |169.6|123.8|36.9%|29.2|27.9|4.6% 256 |175.0|125.7|39.0%|30.0|29.7|1.0% avx512 symmetric blklen|updated prompt tps | baseline prompt tps | prompt tps change%|updated token gen tps | baseline token gen tps | token gen change% -|-|-|-|-|-|- 16 |135.2|156.5|-13.6|25.5|23.8|7.1 32 |150.0|159.5|-5.9|34.9|29.6|17.9 64 |167.5|157.5|6.3|39.7|34.4|15.4 128 |177.8|158.0|12.5|40.3|35.4|13.8 256 |182.6|157.3|16.0|41.7|37.7|10.6 avx512 asymmetric blklen|updated prompt tps | baseline prompt tps | prompt tps change%|updated token gen tps | baseline token gen tps | token gen change% -|-|-|-|-|-|- 16 |136.1|151.4|-10.1%|26.1|19.9|31.1% 32 |150.0|157.8|-4.9%|34.3|29.3|17.0% 64 |165.7|156.6|5.8%|38.7|30.7|26.0% 128 |180.4|156.6|15.1%|40.2|34.7|15.8% 256 |181.3|158.0|14.7%|41.6|36.6|13.6% avx512vnni symmetric blklen|updated prompt tps | baseline prompt tps | prompt tps change%|updated token gen tps | baseline token gen tps | token gen change% -|-|-|-|-|-|- 16 |143.4|155.4|-7.7%|25.6|23.3|9.8% 32 |159.2|157.0|1.4%|34.1|29.8|14.4% 64 |182.0|159.5|14.1%|38.4|34.8|10.3% 128 |221.2|160.8|37.5%|41.0|36.4|12.6% 256 |250.5|162.4|54.2%|41.6|37.7|10.3% avx512vnni asymmetric blklen|updated prompt tps | baseline prompt tps | prompt tps change%|updated token gen tps | baseline token gen tps | token gen change% -|-|-|-|-|-|- 16 |142.5|152.3|-6.4%|26.3|19.7|33.5% 32 |158.2|155.0|2.0%|34.3|29.2|17.4% 64 |184.1|156.6|17.5%|38.3|30.9|23.9% 128 |215.8|156.1|17.5%|41.3|35.0|17.9% 256 |249.2|155.9|59.8%|41.1|36.3|13.2% 4bit gemm implementation with avx using tile. 1. tile size is 2blk by 4. in case of size less then tile, it reduce to 1blk by 4, 2blk by 1 and lastly 1blk by 1. with internal kernel, weight and activation are loaded based on SIMD register width and blk length: avx2 256bit register, 64 weights and activation are loaded. blklen16: 4 blks are computed by the internal kernel blklen32: 2 blks are computed by the internal kernel blklen64: 1 blk are computed by the internal kernel blklen128: 1 blks are computed 2 times by the internal kernel blklen16: 1 blks are computed 4 times by the internal kernel avx512 512bit register, 128 weights and activation are loaded. blklen16: 8 blks are computed by the internal kernel blklen32: 4 blks are computed by the internal kernel blklen64: 2 blk are computed by the internal kernel blklen128: 1 blks are computed by the internal kernel blklen16: 1 blks are computed 2 times by the internal kernel 2. blksum is precomputed during prepacking. computation is reformed: Sum1(scale_a * scale_b * Sum_blk(a_i * b_i)) + Sum2(blksum_a * blksum_b) Sum_blk is over one blk Sum1 is over all blks for one output Sum2 is over all blks for one output Sum is computed with sgemm with the current implementation. Further improvement is possible. --------- Signed-off-by: Liqun Fu <liqfu@microsoft.com> Signed-off-by: liqunfu <liqun.fu@microsoft.com> Signed-off-by: Liqun Fu <liqun_fu@hotmail.com> |
||
|---|---|---|
| .config | ||
| .devcontainer | ||
| .gdn | ||
| .github | ||
| .pipelines | ||
| .vscode | ||
| cgmanifests | ||
| cmake | ||
| csharp | ||
| dockerfiles | ||
| docs | ||
| include/onnxruntime/core | ||
| java | ||
| js | ||
| objectivec | ||
| onnxruntime | ||
| orttraining | ||
| rust | ||
| samples | ||
| tools | ||
| winml | ||
| .clang-format | ||
| .clang-tidy | ||
| .dockerignore | ||
| .gitattributes | ||
| .gitignore | ||
| .gitmodules | ||
| .lintrunner.toml | ||
| build.bat | ||
| build.sh | ||
| build_arm64x.bat | ||
| CITATION.cff | ||
| CODEOWNERS | ||
| CONTRIBUTING.md | ||
| lgtm.yml | ||
| LICENSE | ||
| NuGet.config | ||
| ort.wprp | ||
| ORT_icon_for_light_bg.png | ||
| packages.config | ||
| pyproject.toml | ||
| README.md | ||
| requirements-dev.txt | ||
| requirements-doc.txt | ||
| requirements-lintrunner.txt | ||
| requirements-training.txt | ||
| requirements.txt | ||
| SECURITY.md | ||
| setup.py | ||
| ThirdPartyNotices.txt | ||
| VERSION_NUMBER | ||
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
-
General Information: onnxruntime.ai
-
Usage documentation and tutorials: onnxruntime.ai/docs
-
YouTube video tutorials: youtube.com/@ONNXRuntime
-
Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Builtin Pipeline Status
| System | Inference | Training |
|---|---|---|
| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
Third-party Pipeline Status
| System | Inference | Training |
|---|---|---|
| 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.