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* Merged PR 6093117: Fix test_DynamicQuantizedLinear_max_adjusted_expanded by allowing Identity operator to run on non-float inputs Motivation: As part of the OnnxConformance Backend tests, DynamicQuantizedLinear_max_adjusted_expanded is failing. Root Cause: - The test model has `Identity` operator as one of the node. The input of this node is of non-float data type. - In DML, `Identity` operator is registered as operator which requires floating input. - As per `DirectMLSchema.h`, support for non-float input has been added for `Identity` operator in DML but the same has not been reflected in the `OperatorRegistration.cpp`. Changes: - Removed all traces of the requiresFloatFormatsForGraph flag from it's definition and usage. This flag was only used for Identity and it's related operator. - Added null check for the graphOutput nodeArg in GraphDescBuilder.cpp to stop the crash of the test. Related work items: #33076298 * Merged PR 6103324: Remove usage of non-generic error code (FWP_E_NULL_POINTER) Motivation: Addressing Dwayne comment on the previous PR. [Ref: [6093117](https://dev.azure.com/microsoft/WindowsAI/_git/onnxruntime/pullrequest/6093117?discussionId=44292162&path=%2Fonnxruntime%2Fcore%2Fproviders%2Fdml%2FDmlExecutionProvider%2Fsrc%2FGraphPartitioner.cpp)] Changes: Inside the DML EP, we should not use some other platform specific error codes. Instead we should a appropriate generic error code. Related work items: #33076298 Co-authored-by: Sumit Agarwal <sumitagarwal@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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