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d0519a7603
### Description Two contrib kernels that supposed to speed-up StableDiffusion according to this doc https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/python/tools/transformers/models/stable_diffusion/README.md However, there is no noticable effect in speed or memory consumption. So i guess the only way to make it faster is to implement MultiHeadAttention but i'm not capable of doing that right now. So i'll focus on existing PRs and finding the JSEP kernel that produces incorrect results. It should be one of the old ones (i suspect Conv or ConvTranspose), as SD was not generating images correctly on webgpu since i started working on it. I hoped someone else would fix that by the time i finish with kernels/optimizations 😅 --------- Co-authored-by: Guenther Schmuelling <guschmue@microsoft.com> Co-authored-by: Yulong Wang <7679871+fs-eire@users.noreply.github.com>
98 lines
4.2 KiB
Markdown
98 lines
4.2 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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| ArgMax | ai.onnx(1-10,11-12,13+) | |
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| ArgMin | ai.onnx(1-10,11-12,13+) | |
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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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| BiasAdd | com.microsoft(1+) | |
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| BiasSplitGelu | com.microsoft(1+) | |
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| Cast | ai.onnx(6-8,9-12,13-18,19+) | |
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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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| Concat | ai.onnx(1-3,4-10,11-12,13+) | |
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| Conv | ai.onnx(1-10,11+); com.ms.internal.nhwc(11+) | need perf optimization; conv3d is not supported; need implementing activation |
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| ConvTranspose | ai.onnx(1-10,11+); com.ms.internal.nhwc(11+) | need perf optimization; ConvTranspose3d is 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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| Einsum | ai.onnx(12+) | |
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| Elu | ai.onnx(6+) | |
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| Equal | ai.onnx(7-10,11-12,13-18,19+) | |
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| Erf | ai.onnx(9-12,13+) | |
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| Exp | ai.onnx(6-12,13+) | |
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| Expand | ai.onnx(8-12,13+) | |
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| Flatten | ai.onnx(1-8,9-10,11-12,13+) | |
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| Floor | ai.onnx(6-12,13+) | |
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| Gather | ai.onnx(1-10,11-12,13+) | |
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| GatherElements | ai.onnx(11-12,13+) | |
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| Gelu | com.microsoft(1+) | |
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| Gemm | ai.onnx(7-8,9-10,11-12,13+) | |
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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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| Greater | ai.onnx(7-8,9-12,13+) | |
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| GreaterOrEqual | ai.onnx(12-15,16+) | |
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| If | ai.onnx(1-10,11-12,13-18,19+) | |
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| InstanceNormalization | ai.onnx(6+); com.ms.internal.nhwc(6+) | |
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| LayerNormalization | ai.onnx(17+) | |
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| LeakyRelu | ai.onnx(6-15,16+) | |
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| Less | ai.onnx(7-8,9-12,13+) | |
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| LessOrEqual | ai.onnx(12-15,16+) | |
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| Log | ai.onnx(6-12,13+) | |
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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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| Not | ai.onnx(1+) | |
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| Pad | ai.onnx(2-10,11-12,13-17,18,19+) | |
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| Pow | ai.onnx(7-11,12,13-14,15+) | |
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| Range | ai.onnx(11+) | |
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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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| Resize | ai.onnx(10,11-12,13-17,18,19+); com.ms.internal.nhwc(11-12,13-17,18,19+) | CoordinateTransformMode align_corners is not supported with downsampling |
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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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| SkipLayerNormalization | com.microsoft(1+) | |
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| Slice | ai.onnx(1-9,10,11-12,13+) | |
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| Softmax | ai.onnx(1-10,11-12,13+) | |
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| Split | ai.onnx(1,2-10,11-12,13-17,18+) | |
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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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| Tanh | ai.onnx(6-12,13+) | |
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| ThresholdedRelu | ai.onnx(10+) | |
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| Tile | ai.onnx(6-12,13+) | |
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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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