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ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
2a17d5cf32
### Description This PR contains fusion-level and kernel-level optimizations for [Meta's LLaMA-2](https://blogs.microsoft.com/blog/2023/07/18/microsoft-and-meta-expand-their-ai-partnership-with-llama-2-on-azure-and-windows/). Some of the added optimizations include: - SimplifiedLayerNorm changes - Fusions for multiple variants - SkipSimplifiedLayerNorm changes - Kernel support for CPU - Rotary embeddings (previously did not exist) - Fusions for multiple variants - CPU and CUDA kernels - Supports interleaving and non-interleaving in the same kernels - Optimized cache that requires half of its originally exported sizes - Reduced from `(max_sequence_length, head_size)` to `(max_sequence_length, head_size / 2)` - Multi-head attention - Support for 2D and 3D attention masks - Group query attention (for FP16 CUDA and INT4 CUDA) - Integration with flash attention v2 and past-present buffer sharing - Removes need for `attention_mask` input as it is supported in the kernel - 4 bit quantization - `block_size` parameter is available for customizing - Support the new changes for [Microsoft version](https://github.com/microsoft/Llama-2-Onnx) - Support combinations of the below variants (ex: export ORT version and run with Optimum) Supported variants of LLaMA-2 include: - [ORT version](https://github.com/microsoft/onnxruntime/tree/main/onnxruntime/python/tools/transformers/models/llama) - Produces one ONNX file that is already optimized (and quantized if requested) - Integrates with Optimum - [Another Microsoft version](https://github.com/microsoft/Llama-2-Onnx) - Already exported and available off-the-shelf - Faster versions of those models will be uploaded there soon - [Hugging Face version](https://huggingface.co/meta-llama) - Models that end with `-hf` - Some older and current versions of [`transformers`](https://github.com/huggingface/transformers) and [`optimum`](https://github.com/huggingface/optimum) that export the model to ONNX differently - Note that while some older versions are supported, it is recommended to use the latest package versions. ### Usage To use the optimizations, please see `README.md` for details. Please note the various `requirements.txt` files for the package versions recommended in order to use these changes. To run the ORT transformer optimizer separately, run the script as follows: ``` $ cd onnxruntime/onnxruntime/python/tools/transformers/ $ python3 optimizer.py --input <filename>.onnx --output <filename>.onnx --model_type gpt2 --num_heads <number of attention heads> --hidden_size <attention hidden size> --use_external_data_format --opt_level 0 ``` ### Motivation and Context This PR helps the following issues: - https://github.com/microsoft/onnxruntime/issues/14997 - https://github.com/microsoft/onnxruntime/issues/16254 - https://github.com/microsoft/onnxruntime/issues/17681 - https://github.com/microsoft/onnxruntime/issues/17925 - https://github.com/microsoft/onnxruntime-inference-examples/issues/320 This PR uses changes from the following PRs: - https://github.com/pytorch/pytorch/pull/104468 - https://github.com/pytorch/pytorch/pull/109759 - https://github.com/microsoft/onnxruntime/pull/17020 - https://github.com/microsoft/onnxruntime/pull/17674 - https://github.com/microsoft/onnxruntime/pull/17890 - https://github.com/microsoft/onnxruntime/pull/17920 - https://github.com/huggingface/transformers/pull/26162 - https://github.com/huggingface/optimum/pull/1257 - https://github.com/huggingface/optimum/pull/1289 - https://github.com/huggingface/optimum/pull/1462 ### New TorchDynamo Exporter (experimental stage) This PR uses changes from the following issues and PRs to begin supporting the [new TorchDynamo exporter](https://pytorch.org/docs/stable/onnx.html#torchdynamo-based-onnx-exporter): - https://github.com/huggingface/transformers/pull/26307 - https://github.com/pytorch/pytorch/issues/104903 - https://github.com/pytorch/pytorch/pull/105040 - https://github.com/microsoft/onnxscript/pull/847 - https://github.com/microsoft/onnxscript/pull/862 - https://github.com/microsoft/onnxscript/issues/493 |
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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
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.