8f7b89bd5b
### Description The changes in this PR includes: 1) Fix f16 errors in InstanceNormalization with NCHW format. 2) Use vec to further optimize the original algorithm. 3) (Removed) Don't do layout conversion for InstanceNormalization for JSEP since InstanceNormalization itself is suitable for NCHW layout and has better performance in our current implementation. Tested on sd-vae-decoder-f16.onnx, it becomes 285 ms from 314 ms. The aggregate gpu profiling data can be found as below (Note the data is based change 3).): Before: <html> <body> <!--StartFragment--><span><span class="ui-provider ef bbg bbh bbi bbj bbk bbl bbm bbn bbo bbp bbq bbr bbs bbt bbu bbv bbw bbx bby bbz bca bcb bcc bcd bce bcf bcg bch bci bcj bck bcl bcm bcn" dir="ltr"> Kernel | Time (Ms) | Percentage (%) -- | -- | -- Conv | 201.55 | 69.56 InstanceNormalization | 42.49 | 14.67 Transpose | 28.95 | 9.99 Mul | 5.69 | 1.96 Add | 3.82 | 1.32 MatMul | 3.27 | 1.13 Sigmoid | 2.24 | 0.77 Resize | 1.16 | 0.40 Softmax | 0.34 | 0.12 Cast | 0.24 | 0.08 Sum | 289.75 <br class="Apple-interchange-newline"><!--EndFragment--> </body> </html> After: <html> <body> <!--StartFragment--><span><span class="ui-provider ef bbg bbh bbi bbj bbk bbl bbm bbn bbo bbp bbq bbr bbs bbt bbu bbv bbw bbx bby bbz bca bcb bcc bcd bce bcf bcg bch bci bcj bck bcl bcm bcn" dir="ltr"> Kernel | Time (Ms) | Percentage (%) -- | -- | -- Conv | 205.44 | 79.43 InstanceNormalization | 18.24 | 7.05 Transpose | 17.64 | 6.82 Mul | 5.69 | 2.20 Add | 3.81 | 1.47 MatMul | 3.56 | 1.38 Sigmoid | 2.24 | 0.86 Resize | 1.19 | 0.46 Softmax | 0.59 | 0.23 Cast | 0.24 | 0.09 Sum | 258.65 | </span></span><!--EndFragment--> </body> </html> From above table, we can see that two ops time are greatly reduced. One is InstanceNormalization and the other is Transpose. The reason that the transpose time is reduced is because each InstanceNormalization is surrounded with two reshape ops in sd-vae-decoder-f16.onnx. Due to JSEP is prefer NHWC and InstanceNormalization is layout sensitive op, so two extra transpose ops are inserted dynamically when executing this model. After this change, those inserted transpose ops are not needed anymore. So the overall transpose time is reduced. |
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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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General Information: onnxruntime.ai
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Usage documention and tutorials: onnxruntime.ai/docs
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Companion sample repositories:
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