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Update some op docs for release (#17626)
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### 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. -->
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skottmckay authored Sep 25, 2023
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2 changes: 2 additions & 0 deletions docs/reference/operators/MobileOps.md
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Expand Up @@ -10,6 +10,8 @@ These are the operators and types included in the ORT Mobile pre-built packages

| Release | Documentation |
|---------|---------------|
| 1.16 | [Pre-Built Package Support](./mobile_package_op_type_support_1.16.md)|
| 1.15 | [Pre-Built Package Support](./mobile_package_op_type_support_1.15.md)|
| 1.14 | [Pre-Built Package Support](./mobile_package_op_type_support_1.14.md)|
| 1.13 | [Pre-Built Package Support](./mobile_package_op_type_support_1.13.md)|
| 1.12 | [Pre-Built Package Support](./mobile_package_op_type_support_1.12.md)|
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3 changes: 2 additions & 1 deletion docs/reference/operators/OperatorKernels.md
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Expand Up @@ -5,11 +5,12 @@ grand_parent: Reference
nav_order: 1
---

The operator kernels supported by the CPU Execution Provider and CUDA Execution Provider are documented in the ONNX Runtime repository.
The operator kernels supported by the CPU Execution Provider, CUDA Execution Provider and DML Execution Provider are documented in the ONNX Runtime repository.

| Release | Documentation |
|---------|---------------|
| Current main | [https://github.com/microsoft/onnxruntime/blob/main/docs/OperatorKernels.md](https://github.com/microsoft/onnxruntime/blob/main/docs/OperatorKernels.md) |
| 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)|
| 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)|
| 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)|
| 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 changes: 139 additions & 0 deletions docs/reference/operators/mobile_package_op_type_support_1.15.md
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---
title: ORT 1.15 Mobile Package Operators
parent: Operators
grand_parent: Reference
nav_exclude: true
---

# ONNX Runtime Mobile Pre-Built Package Operator and Type Support

## Supported operators and types

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)

## Supported data input types

- float
- int8_t
- uint8_t

NOTE: Operators used to manipulate dimensions and indices will support int32 and int64.

## Supported Operators

|Operator|Opsets|
|--------|------|
|**ai.onnx**||
|ai.onnx:Abs|12, 13, 14, 15|
|ai.onnx:Add|12, 13, 14, 15|
|ai.onnx:And|12, 13, 14, 15|
|ai.onnx:ArgMax|12, 13, 14, 15|
|ai.onnx:ArgMin|12, 13, 14, 15|
|ai.onnx:AveragePool|12, 13, 14, 15|
|ai.onnx:Cast|12, 13, 14, 15|
|ai.onnx:Ceil|12, 13, 14, 15|
|ai.onnx:Clip|12, 13, 14, 15|
|ai.onnx:Concat|12, 13, 14, 15|
|ai.onnx:ConstantOfShape|12, 13, 14, 15|
|ai.onnx:Conv|12, 13, 14, 15|
|ai.onnx:ConvTranspose|12, 13, 14, 15|
|ai.onnx:Cos|12, 13, 14, 15|
|ai.onnx:CumSum|12, 13, 14, 15|
|ai.onnx:DepthToSpace|12, 13, 14, 15|
|ai.onnx:DequantizeLinear|12, 13, 14, 15|
|ai.onnx:Div|12, 13, 14, 15|
|ai.onnx:DynamicQuantizeLinear|12, 13, 14, 15|
|ai.onnx:Elu|12, 13, 14, 15|
|ai.onnx:Equal|12, 13, 14, 15|
|ai.onnx:Erf|12, 13, 14, 15|
|ai.onnx:Exp|12, 13, 14, 15|
|ai.onnx:Expand|12, 13, 14, 15|
|ai.onnx:Flatten|12, 13, 14, 15|
|ai.onnx:Floor|12, 13, 14, 15|
|ai.onnx:Gather|12, 13, 14, 15|
|ai.onnx:GatherND|12, 13, 14, 15|
|ai.onnx:Gemm|12, 13, 14, 15|
|ai.onnx:GlobalAveragePool|12, 13, 14, 15|
|ai.onnx:Greater|12, 13, 14, 15|
|ai.onnx:GreaterOrEqual|12, 13, 14, 15|
|ai.onnx:HardSigmoid|12, 13, 14, 15|
|ai.onnx:Identity|12, 13, 14, 15|
|ai.onnx:If|12, 13, 14, 15|
|ai.onnx:InstanceNormalization|12, 13, 14, 15|
|ai.onnx:LRN|12, 13, 14, 15|
|ai.onnx:LayerNormalization|1|
|ai.onnx:LeakyRelu|12, 13, 14, 15|
|ai.onnx:Less|12, 13, 14, 15|
|ai.onnx:LessOrEqual|12, 13, 14, 15|
|ai.onnx:Log|12, 13, 14, 15|
|ai.onnx:LogSoftmax|12, 13, 14, 15|
|ai.onnx:Loop|12, 13, 14, 15|
|ai.onnx:MatMul|12, 13, 14, 15|
|ai.onnx:MatMulInteger|12, 13, 14, 15|
|ai.onnx:Max|12, 13, 14, 15|
|ai.onnx:MaxPool|12, 13, 14, 15|
|ai.onnx:Mean|12, 13, 14, 15|
|ai.onnx:Min|12, 13, 14, 15|
|ai.onnx:Mul|12, 13, 14, 15|
|ai.onnx:Neg|12, 13, 14, 15|
|ai.onnx:NonMaxSuppression|12, 13, 14, 15|
|ai.onnx:NonZero|12, 13, 14, 15|
|ai.onnx:Not|12, 13, 14, 15|
|ai.onnx:Or|12, 13, 14, 15|
|ai.onnx:PRelu|12, 13, 14, 15|
|ai.onnx:Pad|12, 13, 14, 15|
|ai.onnx:Pow|12, 13, 14, 15|
|ai.onnx:QLinearConv|12, 13, 14, 15|
|ai.onnx:QLinearMatMul|12, 13, 14, 15|
|ai.onnx:QuantizeLinear|12, 13, 14, 15|
|ai.onnx:Range|12, 13, 14, 15|
|ai.onnx:Reciprocal|12, 13, 14, 15|
|ai.onnx:ReduceMax|12, 13, 14, 15|
|ai.onnx:ReduceMean|12, 13, 14, 15|
|ai.onnx:ReduceMin|12, 13, 14, 15|
|ai.onnx:ReduceProd|12, 13, 14, 15|
|ai.onnx:ReduceSum|12, 13, 14, 15|
|ai.onnx:Relu|12, 13, 14, 15|
|ai.onnx:Reshape|12, 13, 14, 15|
|ai.onnx:Resize|12, 13, 14, 15|
|ai.onnx:ReverseSequence|12, 13, 14, 15|
|ai.onnx:Round|12, 13, 14, 15|
|ai.onnx:Scan|12, 13, 14, 15|
|ai.onnx:ScatterND|12, 13, 14, 15|
|ai.onnx:Shape|12, 13, 14, 15|
|ai.onnx:Sigmoid|12, 13, 14, 15|
|ai.onnx:Sin|12, 13, 14, 15|
|ai.onnx:Size|12, 13, 14, 15|
|ai.onnx:Slice|12, 13, 14, 15|
|ai.onnx:Softmax|12, 13, 14, 15|
|ai.onnx:SpaceToDepth|12, 13, 14, 15|
|ai.onnx:Split|12, 13, 14, 15|
|ai.onnx:Sqrt|12, 13, 14, 15|
|ai.onnx:Squeeze|12, 13, 14, 15|
|ai.onnx:Sub|12, 13, 14, 15|
|ai.onnx:Sum|12, 13, 14, 15|
|ai.onnx:Tanh|12, 13, 14, 15|
|ai.onnx:ThresholdedRelu|12, 13, 14, 15|
|ai.onnx:Tile|12, 13, 14, 15|
|ai.onnx:TopK|12, 13, 14, 15|
|ai.onnx:Transpose|12, 13, 14, 15|
|ai.onnx:Unique|12, 13, 14, 15|
|ai.onnx:Unsqueeze|12, 13, 14, 15|
|ai.onnx:Where|12, 13, 14, 15|
|||
|**com.microsoft**||
|com.microsoft:DynamicQuantizeMatMul|1|
|com.microsoft:FusedConv|1|
|com.microsoft:FusedGemm|1|
|com.microsoft:FusedMatMul|1|
|com.microsoft:Gelu|1|
|com.microsoft:MatMulIntegerToFloat|1|
|com.microsoft:NhwcMaxPool|1|
|com.microsoft:QLinearAdd|1|
|com.microsoft:QLinearAveragePool|1|
|com.microsoft:QLinearConv|1|
|com.microsoft:QLinearGlobalAveragePool|1|
|com.microsoft:QLinearLeakyRelu|1|
|com.microsoft:QLinearMul|1|
|com.microsoft:QLinearSigmoid|1|
|||
139 changes: 139 additions & 0 deletions docs/reference/operators/mobile_package_op_type_support_1.16.md
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---
title: ORT 1.16 Mobile Package Operators
parent: Operators
grand_parent: Reference
nav_exclude: true
---

# ONNX Runtime Mobile Pre-Built Package Operator and Type Support

## Supported operators and types

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)

## Supported data input types

- float
- int8_t
- uint8_t

NOTE: Operators used to manipulate dimensions and indices will support int32 and int64.

## Supported Operators

|Operator|Opsets|
|--------|------|
|**ai.onnx**||
|ai.onnx:Abs|12, 13, 14, 15|
|ai.onnx:Add|12, 13, 14, 15|
|ai.onnx:And|12, 13, 14, 15|
|ai.onnx:ArgMax|12, 13, 14, 15|
|ai.onnx:ArgMin|12, 13, 14, 15|
|ai.onnx:AveragePool|12, 13, 14, 15|
|ai.onnx:Cast|12, 13, 14, 15|
|ai.onnx:Ceil|12, 13, 14, 15|
|ai.onnx:Clip|12, 13, 14, 15|
|ai.onnx:Concat|12, 13, 14, 15|
|ai.onnx:ConstantOfShape|12, 13, 14, 15|
|ai.onnx:Conv|12, 13, 14, 15|
|ai.onnx:ConvTranspose|12, 13, 14, 15|
|ai.onnx:Cos|12, 13, 14, 15|
|ai.onnx:CumSum|12, 13, 14, 15|
|ai.onnx:DepthToSpace|12, 13, 14, 15|
|ai.onnx:DequantizeLinear|12, 13, 14, 15|
|ai.onnx:Div|12, 13, 14, 15|
|ai.onnx:DynamicQuantizeLinear|12, 13, 14, 15|
|ai.onnx:Elu|12, 13, 14, 15|
|ai.onnx:Equal|12, 13, 14, 15|
|ai.onnx:Erf|12, 13, 14, 15|
|ai.onnx:Exp|12, 13, 14, 15|
|ai.onnx:Expand|12, 13, 14, 15|
|ai.onnx:Flatten|12, 13, 14, 15|
|ai.onnx:Floor|12, 13, 14, 15|
|ai.onnx:Gather|12, 13, 14, 15|
|ai.onnx:GatherND|12, 13, 14, 15|
|ai.onnx:Gemm|12, 13, 14, 15|
|ai.onnx:GlobalAveragePool|12, 13, 14, 15|
|ai.onnx:Greater|12, 13, 14, 15|
|ai.onnx:GreaterOrEqual|12, 13, 14, 15|
|ai.onnx:HardSigmoid|12, 13, 14, 15|
|ai.onnx:Identity|12, 13, 14, 15|
|ai.onnx:If|12, 13, 14, 15|
|ai.onnx:InstanceNormalization|12, 13, 14, 15|
|ai.onnx:LRN|12, 13, 14, 15|
|ai.onnx:LayerNormalization|1|
|ai.onnx:LeakyRelu|12, 13, 14, 15|
|ai.onnx:Less|12, 13, 14, 15|
|ai.onnx:LessOrEqual|12, 13, 14, 15|
|ai.onnx:Log|12, 13, 14, 15|
|ai.onnx:LogSoftmax|12, 13, 14, 15|
|ai.onnx:Loop|12, 13, 14, 15|
|ai.onnx:MatMul|12, 13, 14, 15|
|ai.onnx:MatMulInteger|12, 13, 14, 15|
|ai.onnx:Max|12, 13, 14, 15|
|ai.onnx:MaxPool|12, 13, 14, 15|
|ai.onnx:Mean|12, 13, 14, 15|
|ai.onnx:Min|12, 13, 14, 15|
|ai.onnx:Mul|12, 13, 14, 15|
|ai.onnx:Neg|12, 13, 14, 15|
|ai.onnx:NonMaxSuppression|12, 13, 14, 15|
|ai.onnx:NonZero|12, 13, 14, 15|
|ai.onnx:Not|12, 13, 14, 15|
|ai.onnx:Or|12, 13, 14, 15|
|ai.onnx:PRelu|12, 13, 14, 15|
|ai.onnx:Pad|12, 13, 14, 15|
|ai.onnx:Pow|12, 13, 14, 15|
|ai.onnx:QLinearConv|12, 13, 14, 15|
|ai.onnx:QLinearMatMul|12, 13, 14, 15|
|ai.onnx:QuantizeLinear|12, 13, 14, 15|
|ai.onnx:Range|12, 13, 14, 15|
|ai.onnx:Reciprocal|12, 13, 14, 15|
|ai.onnx:ReduceMax|12, 13, 14, 15|
|ai.onnx:ReduceMean|12, 13, 14, 15|
|ai.onnx:ReduceMin|12, 13, 14, 15|
|ai.onnx:ReduceProd|12, 13, 14, 15|
|ai.onnx:ReduceSum|12, 13, 14, 15|
|ai.onnx:Relu|12, 13, 14, 15|
|ai.onnx:Reshape|12, 13, 14, 15|
|ai.onnx:Resize|12, 13, 14, 15|
|ai.onnx:ReverseSequence|12, 13, 14, 15|
|ai.onnx:Round|12, 13, 14, 15|
|ai.onnx:Scan|12, 13, 14, 15|
|ai.onnx:ScatterND|12, 13, 14, 15|
|ai.onnx:Shape|12, 13, 14, 15|
|ai.onnx:Sigmoid|12, 13, 14, 15|
|ai.onnx:Sin|12, 13, 14, 15|
|ai.onnx:Size|12, 13, 14, 15|
|ai.onnx:Slice|12, 13, 14, 15|
|ai.onnx:Softmax|12, 13, 14, 15|
|ai.onnx:SpaceToDepth|12, 13, 14, 15|
|ai.onnx:Split|12, 13, 14, 15|
|ai.onnx:Sqrt|12, 13, 14, 15|
|ai.onnx:Squeeze|12, 13, 14, 15|
|ai.onnx:Sub|12, 13, 14, 15|
|ai.onnx:Sum|12, 13, 14, 15|
|ai.onnx:Tanh|12, 13, 14, 15|
|ai.onnx:ThresholdedRelu|12, 13, 14, 15|
|ai.onnx:Tile|12, 13, 14, 15|
|ai.onnx:TopK|12, 13, 14, 15|
|ai.onnx:Transpose|12, 13, 14, 15|
|ai.onnx:Unique|12, 13, 14, 15|
|ai.onnx:Unsqueeze|12, 13, 14, 15|
|ai.onnx:Where|12, 13, 14, 15|
|||
|**com.microsoft**||
|com.microsoft:DynamicQuantizeMatMul|1|
|com.microsoft:FusedConv|1|
|com.microsoft:FusedGemm|1|
|com.microsoft:FusedMatMul|1|
|com.microsoft:Gelu|1|
|com.microsoft:MatMulIntegerToFloat|1|
|com.microsoft:NhwcMaxPool|1|
|com.microsoft:QLinearAdd|1|
|com.microsoft:QLinearAveragePool|1|
|com.microsoft:QLinearConv|1|
|com.microsoft:QLinearGlobalAveragePool|1|
|com.microsoft:QLinearLeakyRelu|1|
|com.microsoft:QLinearMul|1|
|com.microsoft:QLinearSigmoid|1|
|||

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