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https://github.com/saymrwulf/onnxruntime.git
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### Description Added support for ReduceL1, ReduceL2, ReduceMean, ReduceMin, ReduceMax, ReduceSum, ReduceLogSum, ReduceLogSumExp, ReduceProd and ReduceSquareSum. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> --------- Co-authored-by: Satya Jandhyala <sajandhy@microsoft.com> Co-authored-by: guschmue <guschmue@microsoft.com>
66 lines
2.8 KiB
Markdown
66 lines
2.8 KiB
Markdown
## Operators Support Table
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The following table shows ONNX
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operators and the supported opset domain/versions in WebGPU EP by ONNX Runtime Web. For example,
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`4-6, 8+` means ONNX Runtime Web currently support opset version 4 to 6, 8 and above.
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*This file is automatically generated from the
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def files via [this script](../script/generate-webgpu-operator-md.ts).
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Do not modify directly.*
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| Operator | Opset | Comments |
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|:--------:|:-------------:|-----|
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| Abs | ai.onnx(6-12,13+) | |
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| Acos | ai.onnx(7+) | |
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| Acosh | ai.onnx(9+) | |
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| Add | ai.onnx(7-12,13,14+) | |
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| Asin | ai.onnx(7+) | |
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| Asinh | ai.onnx(9+) | |
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| Atan | ai.onnx(7+) | |
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| Atanh | ai.onnx(9+) | |
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| AveragePool | ai.onnx(7-9,10,11+); com.ms.internal.nhwc(11+) | need perf optimization; need implementing activation |
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| Ceil | ai.onnx(6-12,13+) | |
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| Clip | ai.onnx(6-10,11,12,13+) | |
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| Conv | ai.onnx(1-10,11+); com.ms.internal.nhwc(11+) | need perf optimization; conv3d not supported; need implementing activation |
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| Cos | ai.onnx(7+) | |
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| Cosh | ai.onnx(9+) | |
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| Div | ai.onnx(7-12,13,14+) | |
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| Elu | ai.onnx(6+) | |
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| Erf | ai.onnx(9-12,13+) | |
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| Exp | ai.onnx(6-12,13+) | |
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| Floor | ai.onnx(6-12,13+) | |
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| Gemm | ai.onnx(7-8,9-10,11+) | |
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| GlobalAveragePool | ai.onnx(1+); com.ms.internal.nhwc(1+) | |
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| GlobalMaxPool | ai.onnx(1+); com.ms.internal.nhwc(1+) | |
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| LeakyRelu | ai.onnx(6-15,16+) | |
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| MatMul | ai.onnx(1-12,13+) | |
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| MaxPool | ai.onnx(1-7,8-9,10,11,12+); com.ms.internal.nhwc(11,12+) | need perf optimization; need implementing activation |
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| MemcpyFromHost | ai.onnx(1+) | |
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| MemcpyToHost | ai.onnx(1+) | |
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| Mul | ai.onnx(7-12,13,14+) | |
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| Neg | ai.onnx(6-12,13+) | |
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| Pow | ai.onnx(7-11,12,13-14,15+) | |
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| Reciprocal | ai.onnx(6-12,13+) | |
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| ReduceL1 | ai.onnx(1-10,11-12,13-17,18+) | |
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| ReduceL2 | ai.onnx(1-10,11-12,13-17,18+) | |
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| ReduceLogSum | ai.onnx(1-10,11-12,13-17,18+) | |
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| ReduceLogSumExp | ai.onnx(1-10,11-12,13-17,18+) | |
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| ReduceMax | ai.onnx(1-10,11,12,13-17,18+) | |
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| ReduceMean | ai.onnx(1-10,11-12,13-17,18+) | |
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| ReduceMin | ai.onnx(1-10,11,12,13-17,18+) | |
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| ReduceProd | ai.onnx(1-10,11-12,13-17,18+) | |
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| ReduceSum | ai.onnx(1-10,11-12,13+) | |
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| ReduceSumSquare | ai.onnx(1-10,11-12,13-17,18+) | |
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| Relu | ai.onnx(6-12,13,14+) | |
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| Reshape | ai.onnx(5-12,13,14+) | no GPU kernel |
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| Shape | ai.onnx(1-12,13-14,15+) | no GPU kernel; an ORT warning is generated - need to fix |
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| Sigmoid | ai.onnx(6-12,13+) | |
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| Sin | ai.onnx(7+) | |
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| Sinh | ai.onnx(9+) | |
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| Sqrt | ai.onnx(6-12,13+) | |
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| Squeeze | ai.onnx(1-10,11-12,13+) | |
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| Sub | ai.onnx(7-12,13,14+) | |
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| Tan | ai.onnx(7+) | |
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| ThresholdedRelu | ai.onnx(10+) | |
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| Transpose | ai.onnx(1-12,13+) | need perf optimization |
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| Unsqueeze | ai.onnx(1-10,11-12,13+) | |
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