8140e3fde5
Description: Change requantize interface so it can be processed block by block. This enable as to make requantize to be a post processor of QGEMM. Motivation and Context Previous changes show we improve performance by parallelize batch gemm. Unfortunately we could not parallelize the batch gemm in quantize_linear_matmul due to the requantize operation at the end of each gemm. By changing requantize to be a qgemm post processor, we now can parallelize the batch operation. Co-authored-by: Chen Fu <fuchen@microsoft.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
This project is licensed under the MIT License.