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### Description - Enables int32 support for com.microsoft.DequantizeLinear (contrib op) - Makes the `zero_point` input optional for Quantize/Dequantize contrib ops - Enables QDQ transformations with the Quantize/Dequantize contrib ops - Update tests: EnsureUniqueDQForNodeUnitTests, QDQTransformerTests, TransposeOptimizerTests ### Testing List of tested graph transformations: - [x] QDQSelectorActionTransformer - qdq_transformer_test.cc - [x] QDQS8ToU8Transformer - qdq_transformer_test.cc - [x] DoubleQDQPairsRemover - qdq_transformer_test.cc - [x] IdenticalChildrenConsolidation - qdq_transformer_test.cc - [x] QDQPropagation - qdq_transformer_test.cc - [x] QDQFinalCleanup - qdq_transformer_test.cc - [x] CliQuantFusion - qdq_transformer_test.cc - [x] ReluQuantFusion - qdq_transformer_test.cc - [x] EnsureUniqueDQForNodeUnit - ensure_unique_dq_for_node_unit_test.cc - [x] TransposeOptimizer - transpose_optimizer_test.cc - [x] CommonSubexpressionElimination - graph_transform_test.cc - [x] ConstantFolding - graph_transform_test.cc ### Motivation and Context We need to [support mixed 16-bit/8-bit precision QDQ models](https://github.com/microsoft/onnxruntime/pull/17015). This PR is the first step in achieving this goal: we need to make QDQ contrib ops work with our optimizations/transformations. --------- Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Scott McKay <skottmckay@gmail.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 →
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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Third-party Pipeline Status
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License
This project is licensed under the MIT License.