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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>
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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 is available in the latest versions of Chrome and Edge on Windows, Linux, macOS, Android, and ChromeOS behind an "Enables WebNN API" flag. The operator support status may vary across these platforms. Check the 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', 'count_include_pad' value is 0 |
| 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) |
| 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 | |
| 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 |
| Dropout | ai.onnx(7-9, 10-11, 12, 13-21, 22+) | identity | Only supports test mode |
| 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 | |
| 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 | Only supports 1-D 'C' input |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | The shape of x_scale should be a subsample of the shape of input |
| Reciprocal | ai.onnx(7-12, 13+) | reciprocal | |
| 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 | 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 |