---
title: ORT 1.8 Mobile Package Operators
redirect_from: /docs/reference/mobile/prebuilt-package/mobile_package_op_type_support_1.8
parent: Operators
grand_parent: Reference
nav_exclude: true
---

# ONNX Runtime 1.8: Mobile Package Operator and Type Support

## Supported operators and types

The supported operators and types are based on what is required to support the 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/rel-1.8.0/tools/ci_build/github/android/mobile_package.required_operators.readme.txt)

## Supported data input types

  - float
  - int8_t
  - uint8_t

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

## Supported Operators

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