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Update some op docs for release (#17626)
### Description <!-- Describe your changes. --> Update some ops docs for 1.16 release ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. -->
This commit is contained in:
Scott McKay
GitHub
parent
701728956e
commit
acc1b8b5ea
4 changed files with 282 additions and 1 deletions
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@ -10,6 +10,8 @@ These are the operators and types included in the ORT Mobile pre-built packages
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| Release | Documentation |
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|---------|---------------|
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| 1.16 | [Pre-Built Package Support](./mobile_package_op_type_support_1.16.md)|
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| 1.15 | [Pre-Built Package Support](./mobile_package_op_type_support_1.15.md)|
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| 1.14 | [Pre-Built Package Support](./mobile_package_op_type_support_1.14.md)|
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| 1.13 | [Pre-Built Package Support](./mobile_package_op_type_support_1.13.md)|
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| 1.12 | [Pre-Built Package Support](./mobile_package_op_type_support_1.12.md)|
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@ -5,11 +5,12 @@ grand_parent: Reference
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nav_order: 1
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---
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The operator kernels supported by the CPU Execution Provider and CUDA Execution Provider are documented in the ONNX Runtime repository.
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The operator kernels supported by the CPU Execution Provider, CUDA Execution Provider and DML Execution Provider are documented in the ONNX Runtime repository.
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| Release | Documentation |
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|---------|---------------|
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| Current main | [https://github.com/microsoft/onnxruntime/blob/main/docs/OperatorKernels.md](https://github.com/microsoft/onnxruntime/blob/main/docs/OperatorKernels.md) |
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| 1.16 | [https://github.com/microsoft/onnxruntime/blob/rel-1.16.0/docs/OperatorKernels.md](https://github.com/microsoft/onnxruntime/blob/rel-1.16.0/docs/OperatorKernels.md)|
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| 1.15 | [https://github.com/microsoft/onnxruntime/blob/rel-1.15.0/docs/OperatorKernels.md](https://github.com/microsoft/onnxruntime/blob/rel-1.15.0/docs/OperatorKernels.md)|
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| 1.14 | [https://github.com/microsoft/onnxruntime/blob/rel-1.14.0/docs/OperatorKernels.md](https://github.com/microsoft/onnxruntime/blob/rel-1.14.0/docs/OperatorKernels.md)|
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| 1.13 | [https://github.com/microsoft/onnxruntime/blob/rel-1.13.1/docs/OperatorKernels.md](https://github.com/microsoft/onnxruntime/blob/rel-1.13.1/docs/OperatorKernels.md)|
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139
docs/reference/operators/mobile_package_op_type_support_1.15.md
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139
docs/reference/operators/mobile_package_op_type_support_1.15.md
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@ -0,0 +1,139 @@
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---
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title: ORT 1.15 Mobile Package Operators
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parent: Operators
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grand_parent: Reference
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nav_exclude: true
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---
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# ONNX Runtime Mobile Pre-Built Package Operator and Type Support
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## Supported operators and types
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The supported operators and types are based on what is required to support float32 and quantized versions of popular models. The full list of input models used to determine this list is available [here](https://github.com/microsoft/onnxruntime/blob/main/tools/ci_build/github/android/mobile_package.required_operators.readme.txt)
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## Supported data input types
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- float
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- int8_t
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- uint8_t
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NOTE: Operators used to manipulate dimensions and indices will support int32 and int64.
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## Supported Operators
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|Operator|Opsets|
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|--------|------|
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|**ai.onnx**||
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|ai.onnx:Abs|12, 13, 14, 15|
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|ai.onnx:Add|12, 13, 14, 15|
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|ai.onnx:And|12, 13, 14, 15|
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|ai.onnx:ArgMax|12, 13, 14, 15|
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|ai.onnx:ArgMin|12, 13, 14, 15|
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|ai.onnx:AveragePool|12, 13, 14, 15|
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|ai.onnx:Cast|12, 13, 14, 15|
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|ai.onnx:Ceil|12, 13, 14, 15|
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|ai.onnx:Clip|12, 13, 14, 15|
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|ai.onnx:Concat|12, 13, 14, 15|
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|ai.onnx:ConstantOfShape|12, 13, 14, 15|
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|ai.onnx:Conv|12, 13, 14, 15|
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|ai.onnx:ConvTranspose|12, 13, 14, 15|
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|ai.onnx:Cos|12, 13, 14, 15|
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|ai.onnx:CumSum|12, 13, 14, 15|
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|ai.onnx:DepthToSpace|12, 13, 14, 15|
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|ai.onnx:DequantizeLinear|12, 13, 14, 15|
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|ai.onnx:Div|12, 13, 14, 15|
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|ai.onnx:DynamicQuantizeLinear|12, 13, 14, 15|
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|ai.onnx:Elu|12, 13, 14, 15|
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|ai.onnx:Equal|12, 13, 14, 15|
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|ai.onnx:Erf|12, 13, 14, 15|
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|ai.onnx:Exp|12, 13, 14, 15|
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|ai.onnx:Expand|12, 13, 14, 15|
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|ai.onnx:Flatten|12, 13, 14, 15|
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|ai.onnx:Floor|12, 13, 14, 15|
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|ai.onnx:Gather|12, 13, 14, 15|
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|ai.onnx:GatherND|12, 13, 14, 15|
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|ai.onnx:Gemm|12, 13, 14, 15|
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|ai.onnx:GlobalAveragePool|12, 13, 14, 15|
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|ai.onnx:Greater|12, 13, 14, 15|
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|ai.onnx:GreaterOrEqual|12, 13, 14, 15|
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|ai.onnx:HardSigmoid|12, 13, 14, 15|
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|ai.onnx:Identity|12, 13, 14, 15|
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|ai.onnx:If|12, 13, 14, 15|
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|ai.onnx:InstanceNormalization|12, 13, 14, 15|
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|ai.onnx:LRN|12, 13, 14, 15|
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|ai.onnx:LayerNormalization|1|
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|ai.onnx:LeakyRelu|12, 13, 14, 15|
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|ai.onnx:Less|12, 13, 14, 15|
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|ai.onnx:LessOrEqual|12, 13, 14, 15|
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|ai.onnx:Log|12, 13, 14, 15|
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|ai.onnx:LogSoftmax|12, 13, 14, 15|
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|ai.onnx:Loop|12, 13, 14, 15|
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|ai.onnx:MatMul|12, 13, 14, 15|
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|ai.onnx:MatMulInteger|12, 13, 14, 15|
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|ai.onnx:Max|12, 13, 14, 15|
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|ai.onnx:MaxPool|12, 13, 14, 15|
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|ai.onnx:Mean|12, 13, 14, 15|
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|ai.onnx:Min|12, 13, 14, 15|
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|ai.onnx:Mul|12, 13, 14, 15|
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|ai.onnx:Neg|12, 13, 14, 15|
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|ai.onnx:NonMaxSuppression|12, 13, 14, 15|
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|ai.onnx:NonZero|12, 13, 14, 15|
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|ai.onnx:Not|12, 13, 14, 15|
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|ai.onnx:Or|12, 13, 14, 15|
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|ai.onnx:PRelu|12, 13, 14, 15|
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|ai.onnx:Pad|12, 13, 14, 15|
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|ai.onnx:Pow|12, 13, 14, 15|
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|ai.onnx:QLinearConv|12, 13, 14, 15|
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|ai.onnx:QLinearMatMul|12, 13, 14, 15|
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|ai.onnx:QuantizeLinear|12, 13, 14, 15|
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|ai.onnx:Range|12, 13, 14, 15|
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|ai.onnx:Reciprocal|12, 13, 14, 15|
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|ai.onnx:ReduceMax|12, 13, 14, 15|
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|ai.onnx:ReduceMean|12, 13, 14, 15|
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|ai.onnx:ReduceMin|12, 13, 14, 15|
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|ai.onnx:ReduceProd|12, 13, 14, 15|
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|ai.onnx:ReduceSum|12, 13, 14, 15|
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|ai.onnx:Relu|12, 13, 14, 15|
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|ai.onnx:Reshape|12, 13, 14, 15|
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|ai.onnx:Resize|12, 13, 14, 15|
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|ai.onnx:ReverseSequence|12, 13, 14, 15|
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|ai.onnx:Round|12, 13, 14, 15|
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|ai.onnx:Scan|12, 13, 14, 15|
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|ai.onnx:ScatterND|12, 13, 14, 15|
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|ai.onnx:Shape|12, 13, 14, 15|
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|ai.onnx:Sigmoid|12, 13, 14, 15|
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|ai.onnx:Sin|12, 13, 14, 15|
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|ai.onnx:Size|12, 13, 14, 15|
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|ai.onnx:Slice|12, 13, 14, 15|
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|ai.onnx:Softmax|12, 13, 14, 15|
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|ai.onnx:SpaceToDepth|12, 13, 14, 15|
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|ai.onnx:Split|12, 13, 14, 15|
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|ai.onnx:Sqrt|12, 13, 14, 15|
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|ai.onnx:Squeeze|12, 13, 14, 15|
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|ai.onnx:Sub|12, 13, 14, 15|
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|ai.onnx:Sum|12, 13, 14, 15|
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|ai.onnx:Tanh|12, 13, 14, 15|
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|ai.onnx:ThresholdedRelu|12, 13, 14, 15|
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|ai.onnx:Tile|12, 13, 14, 15|
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|ai.onnx:TopK|12, 13, 14, 15|
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|ai.onnx:Transpose|12, 13, 14, 15|
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|ai.onnx:Unique|12, 13, 14, 15|
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|ai.onnx:Unsqueeze|12, 13, 14, 15|
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|ai.onnx:Where|12, 13, 14, 15|
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|**com.microsoft**||
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|com.microsoft:DynamicQuantizeMatMul|1|
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|com.microsoft:FusedConv|1|
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|com.microsoft:FusedGemm|1|
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|com.microsoft:FusedMatMul|1|
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|com.microsoft:Gelu|1|
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|com.microsoft:MatMulIntegerToFloat|1|
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|com.microsoft:NhwcMaxPool|1|
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|com.microsoft:QLinearAdd|1|
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|com.microsoft:QLinearAveragePool|1|
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|com.microsoft:QLinearConv|1|
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|com.microsoft:QLinearGlobalAveragePool|1|
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|com.microsoft:QLinearLeakyRelu|1|
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|com.microsoft:QLinearMul|1|
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|com.microsoft:QLinearSigmoid|1|
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139
docs/reference/operators/mobile_package_op_type_support_1.16.md
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139
docs/reference/operators/mobile_package_op_type_support_1.16.md
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@ -0,0 +1,139 @@
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---
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title: ORT 1.16 Mobile Package Operators
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parent: Operators
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grand_parent: Reference
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nav_exclude: true
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---
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# ONNX Runtime Mobile Pre-Built Package Operator and Type Support
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## Supported operators and types
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The supported operators and types are based on what is required to support float32 and quantized versions of popular models. The full list of input models used to determine this list is available [here](https://github.com/microsoft/onnxruntime/blob/main/tools/ci_build/github/android/mobile_package.required_operators.readme.txt)
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## Supported data input types
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- float
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- int8_t
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- uint8_t
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NOTE: Operators used to manipulate dimensions and indices will support int32 and int64.
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## Supported Operators
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|Operator|Opsets|
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|--------|------|
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|**ai.onnx**||
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|ai.onnx:Abs|12, 13, 14, 15|
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|ai.onnx:Add|12, 13, 14, 15|
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|ai.onnx:And|12, 13, 14, 15|
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|ai.onnx:ArgMax|12, 13, 14, 15|
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|ai.onnx:ArgMin|12, 13, 14, 15|
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|ai.onnx:AveragePool|12, 13, 14, 15|
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|ai.onnx:Cast|12, 13, 14, 15|
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|ai.onnx:Ceil|12, 13, 14, 15|
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|ai.onnx:Clip|12, 13, 14, 15|
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|ai.onnx:Concat|12, 13, 14, 15|
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|ai.onnx:ConstantOfShape|12, 13, 14, 15|
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|ai.onnx:Conv|12, 13, 14, 15|
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|ai.onnx:ConvTranspose|12, 13, 14, 15|
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|ai.onnx:Cos|12, 13, 14, 15|
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|ai.onnx:CumSum|12, 13, 14, 15|
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|ai.onnx:DepthToSpace|12, 13, 14, 15|
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|ai.onnx:DequantizeLinear|12, 13, 14, 15|
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|ai.onnx:Div|12, 13, 14, 15|
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|ai.onnx:DynamicQuantizeLinear|12, 13, 14, 15|
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|ai.onnx:Elu|12, 13, 14, 15|
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|ai.onnx:Equal|12, 13, 14, 15|
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|ai.onnx:Erf|12, 13, 14, 15|
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|ai.onnx:Exp|12, 13, 14, 15|
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|ai.onnx:Expand|12, 13, 14, 15|
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|ai.onnx:Flatten|12, 13, 14, 15|
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|ai.onnx:Floor|12, 13, 14, 15|
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|ai.onnx:Gather|12, 13, 14, 15|
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|ai.onnx:GatherND|12, 13, 14, 15|
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|ai.onnx:Gemm|12, 13, 14, 15|
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|ai.onnx:GlobalAveragePool|12, 13, 14, 15|
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|ai.onnx:Greater|12, 13, 14, 15|
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|ai.onnx:GreaterOrEqual|12, 13, 14, 15|
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|ai.onnx:HardSigmoid|12, 13, 14, 15|
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|ai.onnx:Identity|12, 13, 14, 15|
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|ai.onnx:If|12, 13, 14, 15|
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|ai.onnx:InstanceNormalization|12, 13, 14, 15|
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|ai.onnx:LRN|12, 13, 14, 15|
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|ai.onnx:LayerNormalization|1|
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|ai.onnx:LeakyRelu|12, 13, 14, 15|
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|ai.onnx:Less|12, 13, 14, 15|
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|ai.onnx:LessOrEqual|12, 13, 14, 15|
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|ai.onnx:Log|12, 13, 14, 15|
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|ai.onnx:LogSoftmax|12, 13, 14, 15|
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|ai.onnx:Loop|12, 13, 14, 15|
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|ai.onnx:MatMul|12, 13, 14, 15|
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|ai.onnx:MatMulInteger|12, 13, 14, 15|
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|ai.onnx:Max|12, 13, 14, 15|
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|ai.onnx:MaxPool|12, 13, 14, 15|
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|ai.onnx:Mean|12, 13, 14, 15|
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|ai.onnx:Min|12, 13, 14, 15|
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|ai.onnx:Mul|12, 13, 14, 15|
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|ai.onnx:Neg|12, 13, 14, 15|
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|ai.onnx:NonMaxSuppression|12, 13, 14, 15|
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|ai.onnx:NonZero|12, 13, 14, 15|
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|ai.onnx:Not|12, 13, 14, 15|
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|ai.onnx:Or|12, 13, 14, 15|
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|ai.onnx:PRelu|12, 13, 14, 15|
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|ai.onnx:Pad|12, 13, 14, 15|
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|ai.onnx:Pow|12, 13, 14, 15|
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|ai.onnx:QLinearConv|12, 13, 14, 15|
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|ai.onnx:QLinearMatMul|12, 13, 14, 15|
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|ai.onnx:QuantizeLinear|12, 13, 14, 15|
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|ai.onnx:Range|12, 13, 14, 15|
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|ai.onnx:Reciprocal|12, 13, 14, 15|
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|ai.onnx:ReduceMax|12, 13, 14, 15|
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|ai.onnx:ReduceMean|12, 13, 14, 15|
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|ai.onnx:ReduceMin|12, 13, 14, 15|
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|ai.onnx:ReduceProd|12, 13, 14, 15|
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|ai.onnx:ReduceSum|12, 13, 14, 15|
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|ai.onnx:Relu|12, 13, 14, 15|
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|ai.onnx:Reshape|12, 13, 14, 15|
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|ai.onnx:Resize|12, 13, 14, 15|
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|ai.onnx:ReverseSequence|12, 13, 14, 15|
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|ai.onnx:Round|12, 13, 14, 15|
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|ai.onnx:Scan|12, 13, 14, 15|
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|ai.onnx:ScatterND|12, 13, 14, 15|
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|ai.onnx:Shape|12, 13, 14, 15|
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|ai.onnx:Sigmoid|12, 13, 14, 15|
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|ai.onnx:Sin|12, 13, 14, 15|
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|ai.onnx:Size|12, 13, 14, 15|
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|ai.onnx:Slice|12, 13, 14, 15|
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|ai.onnx:Softmax|12, 13, 14, 15|
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|ai.onnx:SpaceToDepth|12, 13, 14, 15|
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|ai.onnx:Split|12, 13, 14, 15|
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|ai.onnx:Sqrt|12, 13, 14, 15|
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|ai.onnx:Squeeze|12, 13, 14, 15|
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|ai.onnx:Sub|12, 13, 14, 15|
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|ai.onnx:Sum|12, 13, 14, 15|
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|ai.onnx:Tanh|12, 13, 14, 15|
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|ai.onnx:ThresholdedRelu|12, 13, 14, 15|
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|ai.onnx:Tile|12, 13, 14, 15|
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|ai.onnx:TopK|12, 13, 14, 15|
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|ai.onnx:Transpose|12, 13, 14, 15|
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|ai.onnx:Unique|12, 13, 14, 15|
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|ai.onnx:Unsqueeze|12, 13, 14, 15|
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|ai.onnx:Where|12, 13, 14, 15|
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|**com.microsoft**||
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|com.microsoft:DynamicQuantizeMatMul|1|
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|com.microsoft:FusedConv|1|
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|com.microsoft:FusedGemm|1|
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|com.microsoft:FusedMatMul|1|
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|com.microsoft:Gelu|1|
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|com.microsoft:MatMulIntegerToFloat|1|
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|com.microsoft:NhwcMaxPool|1|
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|com.microsoft:QLinearAdd|1|
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|com.microsoft:QLinearAveragePool|1|
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|com.microsoft:QLinearConv|1|
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|com.microsoft:QLinearGlobalAveragePool|1|
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|com.microsoft:QLinearLeakyRelu|1|
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|com.microsoft:QLinearMul|1|
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|com.microsoft:QLinearSigmoid|1|
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Reference in a new issue