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[WebNN] Remove workarounds for TFLite backend (#23406)
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.
This commit is contained in:
Wanming Lin
GitHub
parent
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commit
18a54284c8
5 changed files with 106 additions and 197 deletions
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@ -6,108 +6,110 @@ operators and the supported opset domain/versions in **WebNN EP** by ONNX Runtim
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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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[WebNN API](https://webmachinelearning.github.io/webnn) provides two device types `cpu` and `gpu` to leverage different on-device accelerators. WebNN API implementation in Chromium uses TFLite XNNPack delegate backend for `cpu` device type and DirectML backend for `gpu` device type. [The op support status](https://webmachinelearning.github.io/webnn-status/) behind these two backends is inconsistent.
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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 | WebNN CPU | WebNN GPU | 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 | ✓ | ✓ | WebNN CPU backend doesn't support casting to uint64 data type |
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| Ceil | ai.onnx(7-12, 13+) | ceil | ✓ | ✓ | |
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| Clip | ai.onnx(7-10, 11, 12, 13+) | clamp | ✓ | ✓ | WebNN CPU backend only supports 3 specific ranges: [0.0, infinity], [-1.0, 1.0], [0.0, 6.0] (Chromium issue: https://issues.chromium.org/issues/326156496) |
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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). WebNN CPU backend only supports default dilations and group |
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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 | ✓ | ✓ | WebNN CPU backend only supports 'alpha' value is 1.0 |
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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 | ✓ | ✓ | WebNN CPU backend restricts the last dimension of input and slope to be same (Chromium issue: https://issues.chromium.org/issues/335517470) |
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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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| 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 | |
|
||||
| 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 | |
|
||||
|
|
|
|||
|
|
@ -17,10 +17,6 @@ class ActivationOpBuilder : public BaseOpBuilder {
|
|||
private:
|
||||
Status AddToModelBuilderImpl(ModelBuilder& model_builder, const Node& node,
|
||||
const logging::Logger& logger) const override ORT_MUST_USE_RESULT;
|
||||
|
||||
// Operator support related.
|
||||
bool IsOpSupportedImpl(const InitializedTensorSet& initializers, const Node& node,
|
||||
WebnnDeviceType device_type, const logging::Logger& logger) const override;
|
||||
};
|
||||
|
||||
// Add operator related.
|
||||
|
|
@ -68,30 +64,6 @@ Status ActivationOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
|
|||
return Status::OK();
|
||||
}
|
||||
|
||||
// Operator support related.
|
||||
bool ActivationOpBuilder::IsOpSupportedImpl(const InitializedTensorSet& /* initializers */,
|
||||
const Node& node,
|
||||
WebnnDeviceType device_type,
|
||||
const logging::Logger& logger) const {
|
||||
const auto& input_defs = node.InputDefs();
|
||||
const auto& op_type = node.OpType();
|
||||
|
||||
std::vector<int64_t> input_shape;
|
||||
if (!GetShape(*input_defs[0], input_shape, logger))
|
||||
return false;
|
||||
|
||||
if (op_type == "Elu" && device_type == WebnnDeviceType::CPU) {
|
||||
NodeAttrHelper helper(node);
|
||||
float alpha = helper.Get("alpha", 1.0f);
|
||||
if (alpha != 1.0f) {
|
||||
LOGS(logger, VERBOSE) << "WebNN CPU backend only supports Elu's alpha == 1.0";
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
void CreateActivationOpBuilder(const std::string& op_type, OpBuilderRegistrations& op_registrations) {
|
||||
if (op_registrations.op_builder_map.count(op_type) > 0)
|
||||
return;
|
||||
|
|
|
|||
|
|
@ -20,8 +20,6 @@ class BinaryOpBuilder : public BaseOpBuilder {
|
|||
const logging::Logger& logger) const override ORT_MUST_USE_RESULT;
|
||||
|
||||
// Operator support related.
|
||||
bool IsOpSupportedImpl(const InitializedTensorSet& initializers, const Node& node,
|
||||
const WebnnDeviceType device_type, const logging::Logger& logger) const override;
|
||||
bool HasSupportedInputsImpl(const InitializedTensorSet& /* initializers */, const Node& node,
|
||||
const emscripten::val& wnn_limits, const logging::Logger& logger) const override;
|
||||
};
|
||||
|
|
@ -59,33 +57,6 @@ Status BinaryOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder, const
|
|||
return Status::OK();
|
||||
}
|
||||
|
||||
bool BinaryOpBuilder::IsOpSupportedImpl(const InitializedTensorSet& initializers,
|
||||
const Node& node,
|
||||
const WebnnDeviceType device_type,
|
||||
const logging::Logger& logger) const {
|
||||
const auto& input_defs = node.InputDefs();
|
||||
const auto& op_type = node.OpType();
|
||||
|
||||
std::vector<int64_t> input0_shape;
|
||||
std::vector<int64_t> input1_shape;
|
||||
if (!GetShape(*input_defs[0], input0_shape, logger) ||
|
||||
!GetShape(*input_defs[1], input1_shape, logger)) {
|
||||
return false;
|
||||
}
|
||||
|
||||
// 'prelu' op in WebNN CPU backend restricts the last dimension of input and slope to be same.
|
||||
// TODO: Remove this workaround once the associated issue is resolved in Chromium:
|
||||
// https://issues.chromium.org/issues/335517470.
|
||||
if (op_type == "PRelu" && device_type == WebnnDeviceType::CPU) {
|
||||
if (input0_shape.back() != input1_shape.back()) {
|
||||
LOGS(logger, VERBOSE) << "The last dimension of input and slope for PRelu must be same for WebNN CPU backend.";
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
bool BinaryOpBuilder::HasSupportedInputsImpl(const InitializedTensorSet& /* initializers */, const Node& node,
|
||||
const emscripten::val& wnn_limits, const logging::Logger& logger) const {
|
||||
const auto& input_defs = node.InputDefs();
|
||||
|
|
|
|||
|
|
@ -69,27 +69,7 @@ bool ClipOpBuilder::IsOpSupportedImpl(const InitializedTensorSet& initializers,
|
|||
// can ensure initializers are constant. See #19401 for details of how this update was made to the NNAPI EP.
|
||||
// GetClipMinMax(graph_viewer, node, minValue, maxValue, logger)
|
||||
float min, max;
|
||||
if (GetClipMinMax(initializers, node, min, max, logger)) {
|
||||
// WebNN CPU backend only supports 3 specific ranges: [0.0, infinity], [-1.0, 1.0], [0.0, 6.0].
|
||||
// TODO: Remove this workaround once the associated issue is resolved in Chromium:
|
||||
// https://issues.chromium.org/issues/326156496.
|
||||
if (device_type == WebnnDeviceType::CPU) {
|
||||
if ((min == 0.0f && max == std::numeric_limits<float>::infinity()) ||
|
||||
(min == -1.0f && max == 1.0f) ||
|
||||
(min == 0.0f && max == 6.0f)) {
|
||||
return true;
|
||||
} else {
|
||||
LOGS(logger, VERBOSE) << "Clip min and max values ("
|
||||
<< min << ", "
|
||||
<< max << ") are not supported for WebNN CPU backend";
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
} else {
|
||||
return false;
|
||||
};
|
||||
return GetClipMinMax(initializers, node, min, max, logger);
|
||||
}
|
||||
|
||||
void CreateClipOpBuilder(const std::string& op_type, OpBuilderRegistrations& op_registrations) {
|
||||
|
|
|
|||
|
|
@ -378,22 +378,6 @@ bool ConvOpBuilder::IsOpSupportedImpl(const InitializedTensorSet& initializers,
|
|||
return false;
|
||||
}
|
||||
|
||||
// WebNN CPU backend (TFLite) only supports default dilations and group.
|
||||
// https://source.chromium.org/chromium/chromium/src/+/main:services/webnn/tflite/graph_builder_tflite.cc;l=1040
|
||||
if (device_type == WebnnDeviceType::CPU && op_type == "ConvTranspose") {
|
||||
NodeAttrHelper helper(node);
|
||||
const auto dilations = helper.Get("dilations", std::vector<int64_t>{1, 1});
|
||||
const auto group = helper.Get("group", 1);
|
||||
if (dilations[0] != 1 || (dilations.size() > 1 && dilations[1] != 1)) {
|
||||
LOGS(logger, VERBOSE) << op_type << " for WebNN CPU backend only supports default dilation 1.";
|
||||
return false;
|
||||
}
|
||||
if (group != 1) {
|
||||
LOGS(logger, VERBOSE) << op_type << " for WebNN CPU backend only supports default group 1.";
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue