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### Description This PR adds an implementation of the `Squeeze` operator to WebGPU JSEP. The implementation follows the [operator schema](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Squeeze) and allows one or two inputs. ### How was it tested 1. I created two models. Without `axes`: ```Python import onnx.helper node = onnx.helper.make_node( "Squeeze", inputs=["T"], outputs=["y"], ) graph = onnx.helper.make_graph([node], "test", [onnx.helper.make_tensor_value_info("T", 1, [3, 1, 4, 5])], [onnx.helper.make_tensor_value_info("y", 1, [3, 4, 5])]) onnx.save(onnx.helper.make_model(graph), "squeeze.onnx") ``` And with `axes`: ```Python import onnx.helper node = onnx.helper.make_node( "Squeeze", inputs=["T", "axes"], outputs=["y"], ) graph = onnx.helper.make_graph([node], "test", [onnx.helper.make_tensor_value_info("T", 1, [3, 1, 4, 5]), onnx.helper.make_tensor_value_info("axes", 7, [1])], [onnx.helper.make_tensor_value_info("y", 1, [3, 4, 5])]) onnx.save(onnx.helper.make_model(graph), "squeeze-dim.onnx") ``` 2. I compiled the runtime using @fs-eire's [instructions](https://gist.github.com/fs-eire/a55b2c7e10a6864b9602c279b8b75dce). 3. I ran the test models in the browser using this minimal setup: ```HTML <html> <script src=".\dist\ort.webgpu.min.js"></script> <script> async function run() { const session = await ort.InferenceSession.create('squeeze-dim.onnx', {executionProviders: ['webgpu']}); console.log(session); const input = new ort.Tensor('float32', new Float32Array(60), [3, 1, 4, 5]); const dim = new ort.Tensor('int64', [-3n], [1]); const output = await session.run({ "T": input, "axes": dim }); console.log(output); } run(); </script> </html> ``` ### Motivation and Context Improve operator coverage for WebGPU JSEP. |
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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
Build Pipeline Status
| System | Inference | Training |
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| Windows | ||
| Linux | ||
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| Web | ||
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Data/Telemetry
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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.