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The WebNN CPU device type may now target different backends, such as CoreML. Legacy special workarounds for the TFLite backend should be removed and allowed to fail as is, as these are implementation issues. Additionally, the WebNN EP should adhere to the WebNN API conformance. We assume all the WebNN ops should be supported, so remove the WebNN op support status for different device types in webnn-operators.md as well.
115 lines
8.4 KiB
Markdown
115 lines
8.4 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 **WebNN EP** by ONNX Runtime Web. For example,
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`7-12, 13+` means ONNX Runtime Web currently supports opset version 7 to 12, 13 and above.
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(**Note**: ONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'.)
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The [WebNN API](https://webmachinelearning.github.io/webnn) is available in the latest versions of Chrome and Edge on Windows,
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Linux, macOS, Android, and ChromeOS behind an <i>"Enables WebNN API"</i> flag. The operator support status may vary across these
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platforms. Check the [WebNN status](https://webmachinelearning.github.io/webnn-status/) for the latest implementation details.
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| Operator | Opset | WebNN API | Comments |
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|:------:|:------:|:------:|:------|
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| Abs | ai.onnx(7-12, 13+) | abs | |
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| Add | ai.onnx(7-12, 13, 14+) | add | |
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| And | ai.onnx(7+) | logicalAnd | |
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| ArgMax | ai.onnx(7-10, 11, 12, 13+) | argMax | |
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| ArgMin | ai.onnx(7-10, 11, 12, 13+) | argMin | |
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| AveragePool | ai.onnx(7-9, 10, 11, 12-18, 19+) | averagePool2d | Only supports 4-D input, 2-D 'kernel_shape', 'count_include_pad' value is 0 |
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| BatchNormalization | ai.onnx(7-8, 9-13, 14, 15+) | batchNormalization | Only supports 'training_mode' value is 0, one output |
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| Cast | ai.onnx(7-8, 9-12, 13-18, 19-20, 21+) | cast | |
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| Ceil | ai.onnx(7-12, 13+) | ceil | |
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| Clip | ai.onnx(7-10, 11, 12, 13+) | clamp | |
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| Concat | ai.onnx(7-10, 11-12, 13+) | concat | |
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| Conv | ai.onnx(7-10, 11+) | conv2d | Only supports 3-D or 4-D input and 'W' (weight) |
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| ConvTranspose | ai.onnx(7-10, 11+) | convTranspose2d | Only supports 3-D or 4-D input and 'W' (weight) |
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| Cos | ai.onnx(7+) | cos | |
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| CumSum | ai.onnx(11-13, 14+) | cumulativeSum | 'axis' input should be a constant |
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| Div | ai.onnx(7-12, 13, 14+) | div | |
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| DequantizeLinear | ai.onnx(10-12, 13-18, 19-20, 21-22, 23+) | dequantizeLinear | The shape of x_scale should be a subsample of the shape of input |
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| Dropout | ai.onnx(7-9, 10-11, 12, 13-21, 22+) | identity | Only supports test mode |
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| Einsum | ai.onnx(12+) | reshape, transpose, matmul, reduceSum, mul, triangular | |
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| Elu | ai.onnx(7+) | elu | |
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| Equal | ai.onnx(7-10, 11-12, 13-18, 19+) | equal | |
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| Erf | ai.onnx(7-9, 10-12, 13+) | erf | |
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| Exp | ai.onnx(7-12, 13+) | exp | |
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| Expand | ai.onnx(8-12, 13+) | expand | 'shape' input should be a constant |
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| Flatten | ai.onnx(7-8, 9-10, 11-12, 13-20, 21+) | reshape | |
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| Floor | ai.onnx(7-12, 13+) | floor | |
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| Gather | ai.onnx(7-10, 11-12, 13+) | gather | |
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| GatherElements | ai.onnx(11-12, 13+) | gatherElements | |
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| GatherND | ai.onnx(11, 12, 13+) | gatherND | Only supports 'batch_dims' == 0 |
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| Gelu | ai.onnx(20+) | gelu | |
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| Gemm | ai.onnx(7-8, 9-10, 11-12, 13+) | gemm | Only supports 1-D 'C' input |
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| GlobalAveragePool | ai.onnx(7+) | averagePool2d | Only supports 4-D input |
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| GlobalMaxPool | ai.onnx(7+) | maxPool2d | Only supports 4-D input |
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| GlobalLpPool| ai.onnx(7+) | l2Pool2d | Only supports 4-D input, 'p' value is 2 |
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| Greater | ai.onnx(7-8, 9-12, 13+) | greater | |
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| GreaterOrEqual | ai.onnx(12-15, 16+) | greaterOrEqual | |
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| GRU | ai.onnx(7-13, 14-21, 22+) | gru | Only supports 'layout' == 0. 'clip' is not supported. The activation functions in 'activations' must be one of 'Relu', 'Tanh', 'Sigmoid'. Forward and backward activations must be the same if bidirectional. 'sequence_lens' if present should be constant with values equal to the first dimension length of input 'X' |
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| HardSigmoid | ai.onnx(7+) | hardSigmoid | |
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| HardSwish | ai.onnx(14+) | hardSwish | |
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| Identity | ai.onnx(7-13, 14-15, 16-18, 19-20, 21+) | identity | |
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| InstanceNormalization | ai.onnx(7+) | instanceNormalization | |
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| LayerNormalization | ai.onnx(7-16, 17+) | layerNormalization | |
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| LeakyRelu | ai.onnx(7-15, 16+) | leakyRelu | |
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| Less | ai.onnx(7-8, 9-12, 13+) | lesser | |
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| LessOrEqual | ai.onnx(12-15, 16+) | lesserOrEqual | |
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| Log | ai.onnx(7-12, 13+) | log | |
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| LpPool | ai.onnx(7-10, 11-17, 18+) | l2Pool2d | Only supports 4-D input, 2-D 'kernel_shape', 'p' value is 2 |
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| LRN | ai.onnx(7-12, 13+) | pad, averagePool2d, transpose, add, mul, pow, div | |
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| LSTM | ai.onnx(7-13, 14-21, 22+) | lstm | Only supports 'layout' == 0, 'input_forget' == 0. 'clip' is not supported. The activation functions in 'activations' must be one of 'Relu', 'Tanh', 'Sigmoid'. Forward and backward activations must be the same if bidirectional. 'sequence_lens' if present should be constant with values equal to the first dimension length of input 'X' |
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| MatMul | ai.onnx(7-8, 9-12, 13+) | matmul | |
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| Max | ai.onnx(7, 8-11, 12, 13+) | max | |
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| MaxPool | ai.onnx(7, 8-9, 10, 11, 12+) | maxPool2d | Only supports 4-D input, 2-D 'kernel_shape', 'storage_order' != 1, one output |
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| Min | ai.onnx(7, 8-11, 12, 13+) | min | |
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| Mul | ai.onnx(7-12, 13, 14+) | mul | |
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| Neg | ai.onnx(7-12, 13+) | neg | |
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| Not | ai.onnx(7+) | logicalNot | |
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| Or | ai.onnx(7+) | logicalOr | |
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| Pad | ai.onnx(7-10, 11-12, 13-17, 18, 19-20, 21+) | pad | modes == 'wrap' is not supported |
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| Pow | ai.onnx(7-11, 12, 13-14, 15+) | pow | |
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| PRelu | ai.onnx(7-8, 9-15, 16+) | prelu | |
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| QuantizeLinear | ai.onnx(10-12, 13-18, 19-20, 21-22, 23+) | quantizeLinear | The shape of x_scale should be a subsample of the shape of input |
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| Reciprocal | ai.onnx(7-12, 13+) | reciprocal | |
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| ReduceL1 | ai.onnx(7-10, 11-12, 13-17, 18+) | reduceL1 | Input 'axes' if present should be a constant |
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| ReduceL2 | ai.onnx(7-10, 11-12, 13-17, 18+) | reduceL2 | Input 'axes' if present should be a constant |
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| ReduceLogSum| ai.onnx(7-10, 11-12, 13-17, 18+) | reduceLogSum | Input 'axes' if present should be a constant |
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| ReduceLogSumExp | ai.onnx(7-10, 11-12, 13-17, 18+) | reduceLogSumExp | Input 'axes' if present should be a constant |
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| ReduceMax | ai.onnx(7-10, 11, 12, 13-17, 18-19, 20+) | reduceMax | Input 'axes' if present should be a constant |
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| ReduceMean | ai.onnx(7-10, 11-12, 13-17, 18+) | reduceMean | Input 'axes' if present should be a constant |
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| ReduceMin | ai.onnx(7-10, 11, 12, 13-17, 18-19, 20+) | reduceMin | Input 'axes' if present should be a constant |
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| ReduceProd | ai.onnx(7-10, 11-12, 13-17, 18+) | reduceProduct | Input 'axes' if present should be a constant |
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| ReduceSum | ai.onnx(7-10, 11-12, 13+) | reduceSum | Input 'axes' if present should be a constant |
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| ReduceSumSquare | ai.onnx(7-10, 11-12, 13-17, 18+) | reduceSumSquare | Input 'axes' if present should be a constant |
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| Relu | ai.onnx(7-12, 13, 14+) | relu | |
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| Reshape | ai.onnx(7-12, 13, 14-18, 19-20, 21+) | reshape | Input 'shape' should be a constant, 0 dimension value in 'shape' is not supported |
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| Resize | ai.onnx(11-12, 13-17, 18, 19+) | resample2d | Only supports 4-D input, antialias == 0, exclude_outside == 0, keep_aspect_ratio_policy == 'stretch', 'linear' and 'nearest' modes, input 'scales' and 'sizes' if present must be a constant |
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| RotaryEmbedding | com.microsoft(1+) | add, concat, gather, mul, reshape, split | |
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| ScatterElements | ai.onnx(11-12, 13-15, 16-17, 18+) | scatterElements | Only supports 'reduction' == 'none' |
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| ScatterND | ai.onnx(11-12, 13-15, 16-17, 18+) | scatterND | Only supports 'reduction' == 'none' |
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| Shape | ai.onnx(7-12, 13-14, 15-18, 19-20, 21+) | slice | |
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| SimplifiedLayerNormalization | ai.onnx(1+) | pow, reduceMean, add, sqrt, div, mul | |
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| Sigmoid | ai.onnx(7-12, 13+) | sigmoid | |
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| Sign | ai.onnx(9-12, 13+) | sign | |
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| SkipSimplifiedLayerNormalization | com.microsoft(1+) | pow, reduceMean, add, sqrt, div, mul | |
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| Softplus | ai.onnx(7+) | softplus | |
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| Softsign | ai.onnx(7+) | softsign | |
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| Sin | ai.onnx(7+) | sin | |
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| Slice | ai.onnx(7-9, 10, 11-12, 13+) | slice, reverse | Input 'starts', 'ends', 'axes', and 'steps' if present must be a constant |
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| Softmax | ai.onnx(7-10, 11-12, 13+) | softmax | |
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| Split | ai.onnx(7-10, 11-12, 13-17, 18+) | split | Input 'split' if present should be a constant |
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| Sqrt | ai.onnx(7-12, 13+) | sqrt | |
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| Squeeze | ai.onnx(7-10, 11-12, 13-20, 21+) | reshape | Input 'axes' if present should be a constant |
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| Sub | ai.onnx(7-12, 13, 14+) | sub | |
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| Tan | ai.onnx(7+) | tan | |
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| Tanh | ai.onnx(7-12, 13+) | tanh | |
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| Tile | ai.onnx(7-12, 13+) | tile | Input 'repeats' should be a constant |
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| Transpose | ai.onnx(7-12, 13-20, 21+) | transpose | |
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| Trilu | ai.onnx(14+) | triangular | Input 'k' (option 'diagonal' for WebNN) if present should be a constant |
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| Unsqueeze | ai.onnx(7-10, 11-12, 13-20, 21+) | reshape | |
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| Where | ai.onnx(7-8, 9-15, 16+) | where | |
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| Xor | ai.onnx(7+) | logicalXor | |
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