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### Description Eliminate Cast operator if Shape is the next one. ### Motivation and Context #### Cast When working with onnx opset 15 and above, the shape operator now accepts all types of variables. This change is documented in the [onnx Changelog](https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Shape-15). As a result, casting variables right before the shape operation becomes unnecessary. Removing these unnecessary casts will improve the graph and potentially provide better performance gains. ## Results On : torchrun examples/onnxruntime/training/language-modeling/run_clm.py --model_name_or_path gpt2 --do_train --overwrite_output_dir --output_dir ./outputs/ --seed 1337 --fp16 True --per_device_train_batch_size 4 --num_train_epochs 1 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 --learning_rate 2e-5 --report_to none --optim adamw_ort_fused without changes: ***** train metrics ***** epoch = 1.0 train_loss = 3.2981 train_runtime = 0:02:13.29 train_samples = 2318 train_samples_per_second = 17.39 train_steps_per_second = 4.351 With my changes: ***** train metrics ***** epoch = 1.0 train_loss = 3.2981 train_runtime = 0:02:08.98 train_samples = 2318 train_samples_per_second = 17.971 train_steps_per_second = 4.497 We see around 3% gain. --------- Co-authored-by: Adam Louly <adamlouly@microsoft.com@orttrainingdev9.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net> |
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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 documention 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
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License
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