## Operators Support Table

The following table shows ONNX
operators and the supported opset domain/versions in **WebNN EP** by ONNX Runtime Web. For example,
`7-12, 13+` means ONNX Runtime Web currently supports opset version 7 to 12, 13 and above.

(**Note**: ONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'.)

The [WebNN API](https://webmachinelearning.github.io/webnn) is available in the latest versions of Chrome and Edge on Windows,
Linux, macOS, Android, and ChromeOS behind an <i>"Enables WebNN API"</i> flag. The operator support status may vary across these
platforms. Check the [WebNN status](https://webmachinelearning.github.io/webnn-status/) for the latest implementation details.


| Operator | Opset | WebNN API | Comments |
|:------:|:------:|:------:|:------|
| Abs | ai.onnx(7-12, 13+) | abs | |
| Add | ai.onnx(7-12, 13, 14+) | add | |
| And | ai.onnx(7+) | logicalAnd | |
| ArgMax | ai.onnx(7-10, 11, 12, 13+) | argMax | |
| ArgMin | ai.onnx(7-10, 11, 12, 13+) | argMin | |
| AveragePool | ai.onnx(7-9, 10, 11, 12-18, 19+) | averagePool2d | Only supports 4-D input, 2-D 'kernel_shape' |
| BatchNormalization | ai.onnx(7-8, 9-13, 14, 15+) | batchNormalization | Only supports 'training_mode' value is 0, one output |
| Cast | ai.onnx(7-8, 9-12, 13-18, 19-20, 21+) | cast | |
| Ceil | ai.onnx(7-12, 13+) | ceil | |
| Clip | ai.onnx(7-10, 11, 12, 13+) | clamp | |
| Concat | ai.onnx(7-10, 11-12, 13+) | concat | |
| Conv | ai.onnx(7-10, 11+) | conv2d | Only supports 3-D or 4-D input and 'W' (weight) |
| ConvInteger | ai.onnx(10+) | cast, conv2d, dequantizeLinear | Only supports 3-D or 4-D input and 'W' (weight) |
| ConvTranspose | ai.onnx(7-10, 11+) | convTranspose2d | Only supports 3-D or 4-D input and 'W' (weight) |
| Cos | ai.onnx(7+) | cos | |
| CumSum | ai.onnx(11-13, 14+) | cumulativeSum | 'axis' input should be a constant |
| Div | ai.onnx(7-12, 13, 14+) | div | |
| DepthToSpace | ai.onnx(7-10, 11-12, 13+) | reshape, transpose | |
| DequantizeLinear | ai.onnx(10-12, 13-18, 19-20, 21-22, 23+) | dequantizeLinear, reshape | |
| Dropout | ai.onnx(7-9, 10-11, 12, 13-21, 22+) | identity | Only supports test mode |
| DynamicQuantizeLinear | ai.onnx(11+) | cast, clamp, div, div, max, min, quantizeLinear, reduceMax, reduceMin, reshape, roundEven, sub | |
| Einsum | ai.onnx(12+) | reshape, transpose, matmul, reduceSum, mul, triangular | |
| Elu | ai.onnx(7+) | elu | |
| Equal | ai.onnx(7-10, 11-12, 13-18, 19+) | equal | |
| Erf | ai.onnx(7-9, 10-12, 13+) | erf | |
| Exp | ai.onnx(7-12, 13+) | exp | |
| Expand | ai.onnx(8-12, 13+) | expand | 'shape' input should be a constant |
| Flatten | ai.onnx(7-8, 9-10, 11-12, 13-20, 21+) | reshape | |
| Floor | ai.onnx(7-12, 13+) | floor | |
| Gather | ai.onnx(7-10, 11-12, 13+) | gather | |
| GatherBlockQuantized | com.microsoft(1+) | dequantizeLinear, gather | |
| GatherElements | ai.onnx(11-12, 13+) | gatherElements | |
| GatherND | ai.onnx(11, 12, 13+) | gatherND | Only supports 'batch_dims' == 0 |
| Gelu | ai.onnx(20+) | gelu | |
| Gemm | ai.onnx(7-8, 9-10, 11-12, 13+) | gemm | |
| GlobalAveragePool | ai.onnx(7+) | averagePool2d | Only supports 4-D input |
| GlobalMaxPool | ai.onnx(7+) | maxPool2d | Only supports 4-D input |
| GlobalLpPool| ai.onnx(7+) | l2Pool2d | Only supports 4-D input, 'p' value is 2 |
| Greater | ai.onnx(7-8, 9-12, 13+) | greater | |
| GreaterOrEqual | ai.onnx(12-15, 16+) | greaterOrEqual | |
| GroupQueryAttention | com.microsoft(1+) | add, cast, concat, constant, cumulativeSum, div, expand, lesser, matmul, reshape, scatterND, softmax, transpose, where | Only supports input total_sequence_length is constant and past_sequence_length of past kv equals to present_sequence_length of present kv. |
| 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' |
| HardSigmoid | ai.onnx(7+) | hardSigmoid | |
| HardSwish | ai.onnx(14+) | hardSwish | |
| Identity | ai.onnx(7-13, 14-15, 16-18, 19-20, 21+) | identity | |
| InstanceNormalization | ai.onnx(7+) | instanceNormalization | |
| IsNaN | ai.onnx(9-12, 13-19, 20+) | isNaN | |
| IsInf | ai.onnx(10-19, 20+) | isInfinite, equal, greater | |
| LayerNormalization | ai.onnx(7-16, 17+) | layerNormalization | |
| LeakyRelu | ai.onnx(7-15, 16+) | leakyRelu | |
| Less | ai.onnx(7-8, 9-12, 13+) | lesser | |
| LessOrEqual | ai.onnx(12-15, 16+) | lesserOrEqual | |
| Log | ai.onnx(7-12, 13+) | log | |
| LpPool | ai.onnx(7-10, 11-17, 18+) | l2Pool2d | Only supports 4-D input, 2-D 'kernel_shape', 'p' value is 2 |
| LRN | ai.onnx(7-12, 13+) | pad, averagePool2d, transpose, add, mul, pow, div | |
| 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' |
| MatMul | ai.onnx(7-8, 9-12, 13+) | matmul | |
| MatMulInteger | ai.onnx(10+) | cast, dequantizeLinear, matmul | |
| MatMulNBits | com.microsoft(1+) | add, dequantizeLinear, matmul, reshape, transpose | Inputs 'B' and 'zero_points' (if present) should be constants, input 'g_idx' is not supported, only bits=4 is supported |
| Max | ai.onnx(7, 8-11, 12, 13+) | max | |
| MaxPool | ai.onnx(7, 8-9, 10, 11, 12+) | maxPool2d | Only supports 4-D input, 2-D 'kernel_shape', 'storage_order' != 1, one output |
| Min | ai.onnx(7, 8-11, 12, 13+) | min | |
| Mul | ai.onnx(7-12, 13, 14+) | mul | |
| MultiHeadAttention | com.microsoft(1+) | add, cast, concat, constant, div, matmul, reshape, softmax, transpose | Does not support inputs bias, key_padding_mask, past_sequence_length, or cache_indirection. Does not support output qk |
| Neg | ai.onnx(7-12, 13+) | neg | |
| Not | ai.onnx(7+) | logicalNot | |
| Or | ai.onnx(7+) | logicalOr | |
| Pad | ai.onnx(7-10, 11-12, 13-17, 18, 19-20, 21+) | pad | modes == 'wrap' is not supported |
| Pow | ai.onnx(7-11, 12, 13-14, 15+) | pow | |
| PRelu | ai.onnx(7-8, 9-15, 16+) | prelu | |
| QuantizeLinear | ai.onnx(10-12, 13-18, 19-20, 21-22, 23+) | quantizeLinear, reshape | |
| Reciprocal | ai.onnx(7-12, 13+) | reciprocal | |
| Round | ai.onnx(11-21, 22+) | roundEven | |
| ReduceL1 | ai.onnx(7-10, 11-12, 13-17, 18+) | reduceL1 | Input 'axes' if present should be a constant |
| ReduceL2 | ai.onnx(7-10, 11-12, 13-17, 18+) | reduceL2 | Input 'axes' if present should be a constant |
| ReduceLogSum| ai.onnx(7-10, 11-12, 13-17, 18+) | reduceLogSum | Input 'axes' if present should be a constant |
| ReduceLogSumExp | ai.onnx(7-10, 11-12, 13-17, 18+) | reduceLogSumExp | Input 'axes' if present should be a constant |
| ReduceMax | ai.onnx(7-10, 11, 12, 13-17, 18-19, 20+) | reduceMax | Input 'axes' if present should be a constant |
| ReduceMean | ai.onnx(7-10, 11-12, 13-17, 18+) | reduceMean | Input 'axes' if present should be a constant |
| ReduceMin | ai.onnx(7-10, 11, 12, 13-17, 18-19, 20+) | reduceMin | Input 'axes' if present should be a constant |
| ReduceProd | ai.onnx(7-10, 11-12, 13-17, 18+) | reduceProduct | Input 'axes' if present should be a constant |
| ReduceSum | ai.onnx(7-10, 11-12, 13+) | reduceSum | Input 'axes' if present should be a constant |
| ReduceSumSquare | ai.onnx(7-10, 11-12, 13-17, 18+) | reduceSumSquare | Input 'axes' if present should be a constant |
| Relu | ai.onnx(7-12, 13, 14+) | relu | |
| 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 |
| 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 |
| RotaryEmbedding | ai.onnx(23+), com.microsoft(1+) | add, concat, gather, mul, reshape, slice, split | |
| ScatterElements | ai.onnx(11-12, 13-15, 16-17, 18+) | scatterElements | Only supports 'reduction' == 'none' |
| ScatterND | ai.onnx(11-12, 13-15, 16-17, 18+) | scatterND | Only supports 'reduction' == 'none' |
| Shape | ai.onnx(7-12, 13-14, 15-18, 19-20, 21+) | slice | |
| SimplifiedLayerNormalization | ai.onnx(1+) | pow, reduceMean, add, sqrt, div, mul | |
| Sigmoid | ai.onnx(7-12, 13+) | sigmoid | |
| Sign | ai.onnx(9-12, 13+) | sign | |
| SkipSimplifiedLayerNormalization | com.microsoft(1+) | pow, reduceMean, add, sqrt, div, mul | |
| Softplus | ai.onnx(7+) | softplus | |
| Softsign | ai.onnx(7+) | softsign | |
| Sin | ai.onnx(7+) | sin | |
| Slice | ai.onnx(7-9, 10, 11-12, 13+) | slice, reverse | Input 'starts', 'ends', 'axes', and 'steps' if present must be a constant |
| Softmax | ai.onnx(7-10, 11-12, 13+) | softmax | |
| Split | ai.onnx(7-10, 11-12, 13-17, 18+) | split | Input 'split' if present should be a constant |
| Sqrt | ai.onnx(7-12, 13+) | sqrt | |
| Squeeze | ai.onnx(7-10, 11-12, 13-20, 21+) | reshape | Input 'axes' if present should be a constant |
| Sub | ai.onnx(7-12, 13, 14+) | sub | |
| Tan | ai.onnx(7+) | tan | |
| Tanh | ai.onnx(7-12, 13+) | tanh | |
| Tile | ai.onnx(7-12, 13+) | tile | Input 'repeats' should be a constant |
| Transpose | ai.onnx(7-12, 13-20, 21+) | transpose | |
| Trilu | ai.onnx(14+) | triangular | Input 'k' (option 'diagonal' for WebNN) if present should be a constant |
| Unsqueeze | ai.onnx(7-10, 11-12, 13-20, 21+) | reshape | |
| Where | ai.onnx(7-8, 9-15, 16+) | where | |
| Xor | ai.onnx(7+) | logicalXor | |
