0ad44d0f79
### Description Fixes a unit test that would fail intermittently due to an existing bug with Pad (reflect mode). When the number of padded values is >= the inner dimension size, the ORT Pad implementation accesses invalid memory. This PR makes the number of padding values less than the inner dimension size to avoid triggering the bug. ### Motivation and Context See related issues: https://github.com/microsoft/onnxruntime/issues/8265 https://github.com/microsoft/onnxruntime/issues/11828 https://github.com/microsoft/onnxruntime/issues/20801 Here's a valgrind trace obtained on a Linux machine (with `sess_options.enable_cpu_mem_arena = False`) ``` ==864228== Invalid read of size 4 ==864228== at 0x2716272A: void onnxruntime::PadInnermostAxis<unsigned int>(unsigned int*, unsigned int*, long, unsigned long) (pad.cc:370) ==864228== by 0x2715D213: onnxruntime::common::Status onnxruntime::PadImpl<unsigned int>(onnxruntime::OpKernelContext*, absl::lts_20240722::InlinedVector<long, 10ul, std::allocator<long> > const&, absl::lts_20240722::InlinedVector<long, 10ul, std::allocator<long> > const&, onnxruntime::Mode const&, unsigned int) (pad.cc:551) ==864228== by 0x2715B2BB: onnxruntime::Pad::Compute(onnxruntime::OpKernelContext*) const (pad.cc:725) ==864228== by 0x276FF6A7: onnxruntime::ExecuteKernel(onnxruntime::StreamExecutionContext&, unsigned long, unsigned long, bool const&, onnxruntime::SessionScope&) (sequential_executor.cc:484) ==864228== by 0x276F4A04: onnxruntime::LaunchKernelStep::Execute(onnxruntime::StreamExecutionContext&, unsigned long, onnxruntime::SessionScope&, bool const&, bool&) (execution_steps.cc:73) ... ``` The above is obtained with the basic Pad(reflect) example on the [ONNX Pad operator spec page](https://onnx.ai/onnx/operators/onnx__Pad.html#summary): ```python data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'reflect' # Expected output by ONNX spec expected_output = [ [1.0, 1.2, 1.0, 1.2], [2.3, 3.4, 2.3, 3.4], [4.5, 5.7, 4.5, 5.7], ] # Bugged output from onnxruntime has invalid/uninitialized data for the first element in the inner dimension # invalid data may be 0.0, inf, nan, etc. ort_output = [ [inf, 1.2, 1.0, 1.2], [inf, 3.4, 2.3, 3.4], [inf, 5.7, 4.5, 5.7], ] ``` |
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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 documentation 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
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| Linux | ||
| Mac | ||
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This project is tested with BrowserStack.
Third-party Pipeline Status
| System | Inference | Training |
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| Linux |  |
Releases
The current release and past releases can be found here: https://github.com/microsoft/onnxruntime/releases.
For details on the upcoming release, including release dates, announcements, features, and guidance on submitting feature requests, please visit the release roadmap: https://onnxruntime.ai/roadmap.
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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.