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### Description This PR is to update the win-ort-main branch to the tip main branch as of 2025-01-23. ### PR List ddf0d377a7 [QNN EP] Add LoggingManager::HasDefaultLogger() to provider bridge API (#23467) 05fbbdf91f [QNN EP] Make QNN EP a shared library (#23120) 1336566d7f Add custom vcpkg ports (#23456) 2e1173c411 Update the compile flags for vcpkg packages (#23455) 1f628a9858 [Mobile] Add BrowserStack Android MAUI Test (#23383) 009cae0ec8 [js/webgpu] Optimize ConvTranspose (Continue) (#23429) 04a4a694cb Use onnx_protobuf.h to suppress some GCC warnings (#23453) 2e3b62b4b0 Suppress some strict-aliasing related warnings in WebGPU EP (#23454) b708f9b1dc Bump ruff from 0.9.1 to 0.9.2 (#23427) c0afc66b2a [WebNN] Remove workarounds for TFLite backend (#23406) 8a821ff7f9 Bump vite from 6.0.7 to 6.0.11 in /js/web/test/e2e/exports/testcases/vite-default (#23446) 220c1a203e Make ORT and Dawn use the same protobuf/abseil source code (#23447) b7b5792147 Change MacOS-13 to ubuntu on for android-java-api-aar-test.yml. (#23444) 19d0d2a30f WIP: Dp4MatMulNBits accuracy level 4 matmul for WebGPU EP (#23365) 95b8effbc4 [QNN EP]: Clean up QNN logging resources if an error occurs during initialization (#23435) 626134c5b5 Bump clang-format from 19.1.6 to 19.1.7 (#23428) 0cf975301f Fix eigen external deps (#23439) f9440aedce Moving RN_CI Android Testing to Linux (#23422) 1aa5902ff4 [QNN EP] workaround for QNN validation bug for Tanh with uint16 quantized output (#23432) 7f5582a0e2 Seperate RN andriod and IOS into 2 separated Stages. (#23400) 73deac2e7f Implement some missing element wise Add/Sub/Mul/Div/Neg operations for CPU and CUDA EPs (#23090) 949fe42af4 Upgrade Java version from react-native/android to Java 17 (#23066) 0892c23463 Update Qnn SDK default version to 2.30 (#23411) 94c099bcec Fix type cast build error (#23423) d633e571d1 [WebNN EP] Fix AddInitializersToSkip issues (#23354) e988ef00e2 [QNN EP] Fix regression for MatMul with two quantized/dynamic uint16 inputs (#23419) 7538795f6b Update onnxruntime binary size checks ci pipeline's docker image (#23405) 6c5ea41cad Revert "[QNN EP] Clean up correctly from a partial setup (#23320)" (#23420) e866804bbe Enable comprehension simplification in ruff rules (#23414) 0a5f1f392c bugfix: string_view of invalid memory (#23417) 4cc38e0277 fix crash when first input of BatchNormalization is 1-D (#23387) 033441487f Target py310 and modernize codebase with ruff (#23401) 87341ac010 [QNN EP] Fix segfault when unregistering HTP shared memory handles (#23402) ### Motivation and Context This update includes the change to make QNN-EP a shared library. --------- Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: Adrian Lizarraga <adlizarraga@microsoft.com> Co-authored-by: Justin Chu <justinchuby@users.noreply.github.com> Co-authored-by: Yulong Wang <7679871+fs-eire@users.noreply.github.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Changming Sun <chasun@microsoft.com> Co-authored-by: Peishen Yan <peishen.yan@intel.com> Co-authored-by: Tianlei Wu <tlwu@microsoft.com> Co-authored-by: Hector Li <hecli@microsoft.com> Co-authored-by: Jian Chen <cjian@microsoft.com> Co-authored-by: Alexis Tsogias <1114095+Zyrin@users.noreply.github.com> Co-authored-by: junchao-zhao <68935141+junchao-loongson@users.noreply.github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: sushraja-msft <44513542+sushraja-msft@users.noreply.github.com> Co-authored-by: Wanming Lin <wanming.lin@intel.com> Co-authored-by: Jiajia Qin <jiajiaqin@microsoft.com> Co-authored-by: Caroline Zhu <wolfivyaura@gmail.com>
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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