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review.mlplatform.org / tosa / specification / refs/tags/v0.80.0 / . / tosa.xml
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<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE tosa SYSTEM "tosa.dtd">
<tosa>
<version major="0" minor="80" patch="0" draft="false"/>
<profiles>
<profile name="BI">Base Inference</profile>
<profile name="MI">Main Inference</profile>
<profile name="MT">Main Training</profile>
</profiles>
<levels>
<level name="none" max_rank="32" max_kernel="2147483647" max_stride="2147483647" max_scale="2048" max_log2_size="63" max_nesting="256">No level</level>
<level name="8K" max_rank="6" max_kernel="8192" max_stride="8192" max_scale="256" max_log2_size="31" max_nesting="6">Level 8K</level>
</levels>
<operators>
<operatorgroup name="tensor">
<operator>
<name>ARGMAX</name>
<arguments>
<argument category="input" name="input" type="tensor_t" shape="shape1" tensor-element-type="in_t">
<description>Input tensor</description>
<levellimit value="rank(shape1)" limit="MAX_RANK"/>
<rank min="1" max="MAX_RANK"/>
</argument>
<argument category="attribute" name="axis" type="tensor_t" shape="-" tensor-element-type="i32_t">
<description>Axis in range from 0 to rank(shape1) - 1</description>
<rank min="0" max="0"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="out_t">
<description>Output tensor, with rank = rank(shape1) - 1</description>
<rank min="0" max="MAX_RANK - 1"/>
</argument>
</arguments>
<types>
<type name='in_t' />
<type name='out_t' />
</types>
<typesupport mode="signed 8" in_t="i8_t" out_t="i32_t"/>
<typesupport mode="signed 16" in_t="i16_t" out_t="i32_t" />
<typesupport mode="fp16" in_t="fp16_t" out_t="i32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_t="bf16_t" out_t="i32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_t="fp32_t" out_t="i32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>AVG_POOL2D</name>
<arguments>
<argument category="input" name="input" type="tensor_t" shape="[N,IH,IW,C]" tensor-element-type="in_out_t">
<description>Input tensor</description>
<rank min="4" max="4"/>
</argument>
<argument category="attribute" name="kernel" type="tensor_t" shape="[2]" tensor-element-type="i32_t">
<description>[kernel_y, kernel_x]</description>
<levellimit value="kernel_y" limit="MAX_KERNEL"/>
<levellimit value="kernel_x" limit="MAX_KERNEL"/>
<rank min="1" max="1"/>
</argument>
<argument category="attribute" name="stride" type="tensor_t" shape="[2]" tensor-element-type="i32_t">
<description>[stride_y, stride_x]</description>
<levellimit value="stride_y" limit="MAX_STRIDE"/>
<levellimit value="stride_x" limit="MAX_STRIDE"/>
<rank min="1" max="1"/>
</argument>
<argument category="attribute" name="pad" type="tensor_t" shape="[4]" tensor-element-type="i32_t">
<description>[pad_top, pad_bottom, pad_left, pad_right]</description>
<levellimit value="pad_top" limit="MAX_KERNEL"/>
<levellimit value="pad_bottom" limit="MAX_KERNEL"/>
<levellimit value="pad_left" limit="MAX_KERNEL"/>
<levellimit value="pad_right" limit="MAX_KERNEL"/>
<rank min="1" max="1"/>
</argument>
<argument category="attribute" name="acc_size" type="tensor_t" shape="-" tensor-element-type="acc_size_t">
<description>Enumerated type, must be one of INT32, FP16, FP32, as defined in the Supported Data Types table for this operation</description>
<rank min="0" max="0"/>
</argument>
<argument category="attribute" name="input_zp" type="tensor_t" shape="-" tensor-element-type="in_out_t">
<description>Input tensor zero point. Must be zero for non-int8 types.</description>
<rank min="0" max="0"/>
</argument>
<argument category="attribute" name="output_zp" type="tensor_t" shape="-" tensor-element-type="in_out_t">
<description>Output tensor zero point. Must be zero for non-int8 types.</description>
<rank min="0" max="0"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="[N,OH,OW,C]" tensor-element-type="in_out_t">
<description>Output tensor 4D</description>
<rank min="4" max="4"/>
</argument>
</arguments>
<types>
<type name='in_out_t' />
<type name='acc_t' />
</types>
<typesupport mode="signed 8 with int32 accumulate" in_out_t="i8_t" acc_t="i32_t" />
<typesupport mode="signed 16 with int32 accumulate" in_out_t="i16_t" acc_t="i32_t" />
<typesupport mode="fp16 with fp16 accumulate" in_out_t="fp16_t" acc_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp16 with fp32 accumulate" in_out_t="fp16_t" acc_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16 with fp32 accumulate" in_out_t="bf16_t" acc_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32 with fp32 accumulate" in_out_t="fp32_t" acc_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>CONV2D</name>
<arguments>
<argument category="input" name="input" type="tensor_t" shape="[N,IH,IW,IC]" tensor-element-type="in_t">
<description>Input tensor</description>
<rank min="4" max="4"/>
</argument>
<argument category="input" name="weight" type="tensor_t" shape="[OC,KH,KW,IC]" tensor-element-type="weight_t">
<description>Weight kernel size KH x KW</description>
<levellimit value="dilation_y * KH" limit="MAX_KERNEL"/>
<levellimit value="dilation_x * KW" limit="MAX_KERNEL"/>
<rank min="4" max="4"/>
</argument>
<argument category="input" name="bias" type="tensor_t" shape="[BC]" tensor-element-type="out_t">
<description>Per output channel bias data.</description>
<rank min="1" max="1"/>
</argument>
<argument category="attribute" name="pad" type="tensor_t" shape="[4]" tensor-element-type="i32_t">
<description>[pad_top, pad_bottom, pad_left, pad_right]</description>
<levellimit value="pad_top" limit="MAX_KERNEL"/>
<levellimit value="pad_bottom" limit="MAX_KERNEL"/>
<levellimit value="pad_left" limit="MAX_KERNEL"/>
<levellimit value="pad_right" limit="MAX_KERNEL"/>
<rank min="1" max="1"/>
</argument>
<argument category="attribute" name="stride" type="tensor_t" shape="[2]" tensor-element-type="i32_t">
<description>[stride_y, stride_x]</description>
<levellimit value="stride_y" limit="MAX_STRIDE"/>
<levellimit value="stride_x" limit="MAX_STRIDE"/>
<rank min="1" max="1"/>
</argument>
<argument category="attribute" name="dilation" type="tensor_t" shape="[2]" tensor-element-type="i32_t">
<description>[dilation_y, dilation_x]</description>
<rank min="1" max="1"/>
</argument>
<argument category="attribute" name="input_zp" type="tensor_t" shape="-" tensor-element-type="in_t">
<description>Input tensor zero point. Must be zero for non-int8 types.</description>
<rank min="0" max="0"/>
</argument>
<argument category="attribute" name="weight_zp" type="tensor_t" shape="-" tensor-element-type="weight_t">
<description>Weight zero point. Must be zero for non-int8 types.</description>
<rank min="0" max="0"/>
</argument>
<argument category="attribute" name="local_bound" type="tensor_t" shape="-" tensor-element-type="bool_t" optional="true">
<description>
This optional attribute affects the floating-point compliance error bound.
The default of false allows for direct and transform based, fast convolution algorithms.
Only set to true if direct dot-product calculation precision is required.
</description>
<rank min="0" max="0"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="[N,OH,OW,OC]" tensor-element-type="out_t">
<description>Output tensor</description>
<rank min="4" max="4"/>
</argument>
</arguments>
<types>
<type name='in_t' />
<type name='weight_t' />
<type name='out_t' />
</types>
<typesupport mode="signed 8x8 with int32 accumulate" in_t="i8_t" weight_t="i8_t" out_t="i32_t" />
<typesupport mode="signed 8x4 with int32 accumulate" in_t="i8_t" weight_t="i4_t" out_t="i32_t" />
<typesupport mode="signed 16x8 with int48 accumulate" in_t="i16_t" weight_t="i8_t" out_t="i48_t" />
<typesupport mode="fp16 with fp16 accumulate" in_t="fp16_t" weight_t="fp16_t" out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp16 with fp32 accumulate" in_t="fp16_t" weight_t="fp16_t" out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16 with fp32 accumulate" in_t="bf16_t" weight_t="bf16_t" out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32 with fp32 accumulate" in_t="fp32_t" weight_t="fp32_t" out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>CONV3D</name>
<arguments>
<argument category="input" name="input" type="tensor_t" shape="[N,ID,IH,IW,IC]" tensor-element-type="in_t">
<description>Input tensor</description>
<rank min="5" max="5"/>
</argument>
<argument category="input" name="weight" type="tensor_t" shape="[OC,KD,KH,KW,IC]" tensor-element-type="weight_t">
<description>Weight kernel size KDxKHxKW</description>
<levellimit value="dilation_d * KD" limit="MAX_KERNEL"/>
<levellimit value="dilation_y * KH" limit="MAX_KERNEL"/>
<levellimit value="dilation_x * KW" limit="MAX_KERNEL"/>
<rank min="5" max="5"/>
</argument>
<argument category="input" name="bias" type="tensor_t" shape="[BC]" tensor-element-type="out_t">
<description>Per output channel bias data.</description>
<rank min="1" max="1"/>
</argument>
<argument category="attribute" name="pad" type="tensor_t" shape="[6]" tensor-element-type="i32_t">
<description>[pad_d0, pad_d1, pad_top, pad_bottom, pad_left, pad_right]</description>
<levellimit value="pad_d0" limit="MAX_KERNEL"/>
<levellimit value="pad_d1" limit="MAX_KERNEL"/>
<levellimit value="pad_top" limit="MAX_KERNEL"/>
<levellimit value="pad_bottom" limit="MAX_KERNEL"/>
<levellimit value="pad_left" limit="MAX_KERNEL"/>
<levellimit value="pad_right" limit="MAX_KERNEL"/>
<rank min="1" max="1"/>
</argument>
<argument category="attribute" name="stride" type="tensor_t" shape="[3]" tensor-element-type="i32_t">
<description>[stride_d, stride_y, stride_x]</description>
<levellimit value="stride_y" limit="MAX_STRIDE"/>
<levellimit value="stride_x" limit="MAX_STRIDE"/>
<levellimit value="stride_d" limit="MAX_STRIDE"/>
<rank min="1" max="1"/>
</argument>
<argument category="attribute" name="dilation" type="tensor_t" shape="[3]" tensor-element-type="i32_t">
<description>[dilation_d, dilation_y, dilation_x]</description>
<rank min="1" max="1"/>
</argument>
<argument category="attribute" name="input_zp" type="tensor_t" shape="-" tensor-element-type="in_t">
<description>Input tensor zero point. Must be zero for non-int8 types.</description>
<rank min="0" max="0"/>
</argument>
<argument category="attribute" name="weight_zp" type="tensor_t" shape="-" tensor-element-type="weight_t">
<description>Weight zero point. Must be zero for non-int8 types.</description>
<rank min="0" max="0"/>
</argument>
<argument category="attribute" name="local_bound" type="tensor_t" shape="-" tensor-element-type="bool_t" optional="true">
<description>
This optional attribute affects the floating-point compliance error bound.
The default of false allows for direct and transform based, fast convolution algorithms.
Only set to true if direct dot-product calculation precision is required.
</description>
<rank min="0" max="0"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="[N,OD,OH,OW,OC]" tensor-element-type="out_t">
<description>Output tensor</description>
<rank min="5" max="5"/>
</argument>
</arguments>
<types>
<type name='in_t' />
<type name='weight_t' />
<type name='out_t' />
</types>
<typesupport mode="signed 8x8 with int32 accumulate" in_t="i8_t" weight_t="i8_t" out_t="i32_t" />
<typesupport mode="signed 8x4 with int32 accumulate" in_t="i8_t" weight_t="i4_t" out_t="i32_t" />
<typesupport mode="signed 16x8 with int48 accumulate" in_t="i16_t" weight_t="i8_t" out_t="i48_t" />
<typesupport mode="fp16 with fp16 accumulate" in_t="fp16_t" weight_t="fp16_t" out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp16 with fp32 accumulate" in_t="fp16_t" weight_t="fp16_t" out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16 with fp32 accumulate" in_t="bf16_t" weight_t="bf16_t" out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32 with fp32 accumulate" in_t="fp32_t" weight_t="fp32_t" out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>DEPTHWISE_CONV2D</name>
<arguments>
<argument category="input" name="input" type="tensor_t" shape="[N,H,W,C]" tensor-element-type="in_t">
<description>Input tensor</description>
<rank min="4" max="4"/>
</argument>
<argument category="input" name="weight" type="tensor_t" shape="[KH,KW,C,M]" tensor-element-type="weight_t">
<description>Weight kernel size KH x KW</description>
<levellimit value="dilation_y * KH" limit="MAX_KERNEL"/>
<levellimit value="dilation_x * KW" limit="MAX_KERNEL"/>
<rank min="4" max="4"/>
</argument>
<argument category="input" name="bias" type="tensor_t" shape="[BC]" tensor-element-type="out_t">
<description>Per output channel bias data.</description>
<rank min="1" max="1"/>
</argument>
<argument category="attribute" name="pad" type="tensor_t" shape="[4]" tensor-element-type="i32_t">
<description>[pad_top, pad_bottom, pad_left, pad_right]</description>
<levellimit value="pad_top" limit="MAX_KERNEL"/>
<levellimit value="pad_bottom" limit="MAX_KERNEL"/>
<levellimit value="pad_left" limit="MAX_KERNEL"/>
<levellimit value="pad_right" limit="MAX_KERNEL"/>
<rank min="1" max="1"/>
</argument>
<argument category="attribute" name="stride" type="tensor_t" shape="[2]" tensor-element-type="i32_t">
<description>[stride_y, stride_x]</description>
<levellimit value="stride_y" limit="MAX_STRIDE"/>
<levellimit value="stride_x" limit="MAX_STRIDE"/>
<rank min="1" max="1"/>
</argument>
<argument category="attribute" name="dilation" type="tensor_t" shape="[2]" tensor-element-type="i32_t">
<description>[dilation_y, dilation_x]</description>
<rank min="1" max="1"/>
</argument>
<argument category="attribute" name="input_zp" type="tensor_t" shape="-" tensor-element-type="in_t">
<description>Input tensor zero point. Must be zero for non-int8 types.</description>
<rank min="0" max="0"/>
</argument>
<argument category="attribute" name="weight_zp" type="tensor_t" shape="-" tensor-element-type="weight_t">
<description>Weight zero point. Must be zero for non-int8 types.</description>
<rank min="0" max="0"/>
</argument>
<argument category="attribute" name="local_bound" type="tensor_t" shape="-" tensor-element-type="bool_t" optional="true">
<description>
This optional attribute affects the floating-point compliance error bound.
The default of false allows for direct and transform based, fast convolution algorithms.
Only set to true if direct dot-product calculation precision is required.
</description>
<rank min="0" max="0"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="[N,OH,OW,C*M]" tensor-element-type="out_t">
<description>Output tensor</description>
<rank min="4" max="4"/>
</argument>
</arguments>
<types>
<type name='in_t' />
<type name='weight_t' />
<type name='out_t' />
</types>
<typesupport mode="signed 8x8 with int32 accumulate" in_t="i8_t" weight_t="i8_t" out_t="i32_t" />
<typesupport mode="signed 8x4 with int32 accumulate" in_t="i8_t" weight_t="i4_t" out_t="i32_t" />
<typesupport mode="signed 16x8 with int48 accumulate" in_t="i16_t" weight_t="i8_t" out_t="i48_t" />
<typesupport mode="fp16 with fp16 accumulate" in_t="fp16_t" weight_t="fp16_t" out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp16 with fp32 accumulate" in_t="fp16_t" weight_t="fp16_t" out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16 with fp32 accumulate" in_t="bf16_t" weight_t="bf16_t" out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32 with fp32 accumulate" in_t="fp32_t" weight_t="fp32_t" out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>FFT2D</name>
<arguments>
<argument category="input" name="input_real" type="tensor_t" shape="[N,H,W]" tensor-element-type="in_out_t">
<description>Real part of the complex input. H,W must be powers of two.</description>
<levellimit value="H" limit="MAX_KERNEL"/>
<levellimit value="W" limit="MAX_KERNEL"/>
<rank min="3" max="3"/>
</argument>
<argument category="input" name="input_imag" type="tensor_t" shape="[N,H,W]" tensor-element-type="in_out_t">
<description>Imaginary part of the complex input. H,W must be powers of two.</description>
<rank min="3" max="3"/>
</argument>
<argument category="attribute" name="inverse" type="tensor_t" shape="-" tensor-element-type="bool_t">
<description>false for forward FFT, true for inverse FFT</description>
<rank min="0" max="0"/>
</argument>
<argument category="output" name="output_real" type="tensor_t" shape="[N,H,W]" tensor-element-type="in_out_t">
<description>Real part of the complex output.</description>
<rank min="3" max="3"/>
</argument>
<argument category="attribute" name="local_bound" type="tensor_t" shape="-" tensor-element-type="bool_t" optional="true">
<description>
This optional attribute affects the floating-point compliance error bound.
The default of false allows for direct and transform based, fast convolution algorithms.
Only set to true if direct dot-product calculation precision is required.
</description>
<rank min="0" max="0"/>
</argument>
<argument category="output" name="output_imag" type="tensor_t" shape="[N,H,W]" tensor-element-type="in_out_t">
<description>Imaginary part of the complex output.</description>
<rank min="3" max="3"/>
</argument>
</arguments>
<types>
<type name='in_out_t' />
</types>
<typesupport mode="fp32" in_out_t="fp32_t" >
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>FULLY_CONNECTED</name>
<arguments>
<argument category="input" name="input" type="tensor_t" shape="[N,IC]" tensor-element-type="in_t">
<description>Input tensor</description>
<rank min="2" max="2"/>
</argument>
<argument category="input" name="weight" type="tensor_t" shape="[OC,IC]" tensor-element-type="weight_t">
<description>Weights</description>
<rank min="2" max="2"/>
</argument>
<argument category="input" name="bias" type="tensor_t" shape="[BC]" tensor-element-type="out_t">
<description>Per output channel bias data.</description>
<rank min="1" max="1"/>
</argument>
<argument category="attribute" name="input_zp" type="tensor_t" shape="-" tensor-element-type="in_t">
<description>Input tensor zero point. Must be zero for non-int8 types.</description>
<rank min="0" max="0"/>
</argument>
<argument category="attribute" name="weight_zp" type="tensor_t" shape="-" tensor-element-type="weight_t">
<description>Weight zero point. Must be zero for non-int8 types.</description>
<rank min="0" max="0"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="[N,OC]" tensor-element-type="out_t">
<description>Output tensor</description>
<rank min="2" max="2"/>
</argument>
</arguments>
<types>
<type name='in_t' />
<type name='weight_t' />
<type name='out_t' />
</types>
<typesupport mode="signed 8x8 with int32 accumulate" in_t="i8_t" weight_t="i8_t" out_t="i32_t" />
<typesupport mode="signed 8x4 with int32 accumulate" in_t="i8_t" weight_t="i4_t" out_t="i32_t" />
<typesupport mode="signed 16x8 with int48 accumulate" in_t="i16_t" weight_t="i8_t" out_t="i48_t" />
<typesupport mode="fp16 with fp16 accumulate" in_t="fp16_t" weight_t="fp16_t" out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp16 with fp32 accumulate" in_t="fp16_t" weight_t="fp16_t" out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16 with fp32 accumulate" in_t="bf16_t" weight_t="bf16_t" out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32 with fp32 accumulate" in_t="fp32_t" weight_t="fp32_t" out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>MATMUL</name>
<arguments>
<argument category="input" name="A" type="tensor_t" shape="[N,H,C]" tensor-element-type="in_t">
<description>Input tensor A, N matrices of size HxC</description>
<rank min="3" max="3"/>
</argument>
<argument category="input" name="B" type="tensor_t" shape="[N,C,W]" tensor-element-type="in_t">
<description>Input tensor B, N matrices of size CxW</description>
<rank min="3" max="3"/>
</argument>
<argument category="attribute" name="A_zp" type="tensor_t" shape="-" tensor-element-type="in_t">
<description>Input tensor A zero point. Must be zero for non-int8 types.</description>
<rank min="0" max="0"/>
</argument>
<argument category="attribute" name="B_zp" type="tensor_t" shape="-" tensor-element-type="in_t">
<description>Input tensor B zero point. Must be zero for non-int8 types.</description>
<rank min="0" max="0"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="[N,H,W]" tensor-element-type="out_t">
<description>Output tensor, N matrices of size HxW</description>
<rank min="3" max="3"/>
</argument>
</arguments>
<types>
<type name='in_t' />
<type name='out_t' />
</types>
<typesupport mode="signed 8x8 with int32 accumulate" in_t="i8_t" out_t="i32_t" />
<typesupport mode="signed 16x16 with int48 accumulate" in_t="i16_t" out_t="i48_t" />
<typesupport mode="fp16 with fp16 accumulate" in_t="fp16_t" out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp16 with fp32 accumulate" in_t="fp16_t" out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16 with fp32 accumulate" in_t="bf16_t" out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32 with fp32 accumulate" in_t="fp32_t" out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>MAX_POOL2D</name>
<arguments>
<argument category="input" name="input" type="tensor_t" shape="[N,IH,IW,C]" tensor-element-type="in_out_t">
<description>Input tensor 4D</description>
<rank min="4" max="4"/>
</argument>
<argument category="attribute" name="kernel" type="tensor_t" shape="[2]" tensor-element-type="i32_t">
<description>[kernel_y, kernel_x]</description>
<levellimit value="kernel_y" limit="MAX_KERNEL"/>
<levellimit value="kernel_x" limit="MAX_KERNEL"/>
<rank min="1" max="1"/>
</argument>
<argument category="attribute" name="stride" type="tensor_t" shape="[2]" tensor-element-type="i32_t">
<description>[stride_y, stride_x]</description>
<levellimit value="stride_y" limit="MAX_STRIDE"/>
<levellimit value="stride_x" limit="MAX_STRIDE"/>
<rank min="1" max="1"/>
</argument>
<argument category="attribute" name="pad" type="tensor_t" shape="[4]" tensor-element-type="i32_t">
<description>[pad_top, pad_bottom, pad_left, pad_right]</description>
<levellimit value="pad_top" limit="MAX_KERNEL"/>
<levellimit value="pad_bottom" limit="MAX_KERNEL"/>
<levellimit value="pad_left" limit="MAX_KERNEL"/>
<levellimit value="pad_right" limit="MAX_KERNEL"/>
<rank min="1" max="1"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="[N,OH,OW,C]" tensor-element-type="in_out_t">
<description>Output tensor 4D</description>
<rank min="4" max="4"/>
</argument>
</arguments>
<types>
<type name='in_out_t' />
</types>
<typesupport mode="signed 8" in_out_t="i8_t" />
<typesupport mode="signed 16" in_out_t="i16_t" />
<typesupport mode="fp16" in_out_t="fp16_t" >
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t" >
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t" >
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>RFFT2D</name>
<arguments>
<argument category="input" name="input" type="tensor_t" shape="[N,H,W]" tensor-element-type="in_out_t">
<description>Real input. H,W must be powers of two.</description>
<levellimit value="H" limit="MAX_KERNEL"/>
<levellimit value="W" limit="MAX_KERNEL"/>
<rank min="3" max="3"/>
</argument>
<argument category="output" name="output_real" type="tensor_t" shape="[N,H,W/2 + 1]" tensor-element-type="in_out_t">
<description>Real part of the complex output</description>
<rank min="3" max="3"/>
</argument>
<argument category="output" name="output_imag" type="tensor_t" shape="[N,H,W/2 + 1]" tensor-element-type="in_out_t">
<description>Imaginary part of the complex output.</description>
<rank min="3" max="3"/>
</argument>
</arguments>
<types>
<type name='in_out_t' />
</types>
<typesupport mode="fp32" in_out_t="fp32_t" >
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>TRANSPOSE_CONV2D</name>
<arguments>
<argument category="input" name="input" type="tensor_t" shape="[N,IH,IW,IC]" tensor-element-type="in_t">
<description>Input tensor</description>
<rank min="4" max="4"/>
</argument>
<argument category="input" name="weight" type="tensor_t" shape="[OC,KH,KW,IC]" tensor-element-type="weight_t">
<description>Weight kernel size KH x KW</description>
<levellimit value="KH" limit="MAX_KERNEL"/>
<levellimit value="KW" limit="MAX_KERNEL"/>
<rank min="4" max="4"/>
</argument>
<argument category="input" name="bias" type="tensor_t" shape="[BC]" tensor-element-type="out_t">
<description>Per output channel bias data.</description>
<rank min="1" max="1"/>
</argument>
<argument category="attribute" name="out_pad" type="tensor_t" shape="[4]" tensor-element-type="i32_t">
<description>[out_pad_top, out_pad_bottom, out_pad_left, out_pad_right]</description>
<levellimit value="out_pad_top" limit="MAX_KERNEL"/>
<levellimit value="out_pad_bottom" limit="MAX_KERNEL"/>
<levellimit value="out_pad_left" limit="MAX_KERNEL"/>
<levellimit value="out_pad_right" limit="MAX_KERNEL"/>
<rank min="1" max="1"/>
</argument>
<argument category="attribute" name="stride" type="tensor_t" shape="[2]" tensor-element-type="i32_t">
<description>[stride_y, stride_x]</description>
<levellimit value="stride_y" limit="MAX_STRIDE"/>
<levellimit value="stride_x" limit="MAX_STRIDE"/>
<rank min="1" max="1"/>
</argument>
<argument category="attribute" name="out_shape" type="tensor_t" shape="[4]" tensor-element-type="i32_t">
<description>[N,OH,OW,OC]</description>
<rank min="1" max="1"/>
</argument>
<argument category="attribute" name="input_zp" type="tensor_t" shape="-" tensor-element-type="in_t">
<description>Input tensor zero point. Must be zero for non-int8 types.</description>
<rank min="0" max="0"/>
</argument>
<argument category="attribute" name="weight_zp" type="tensor_t" shape="-" tensor-element-type="weight_t">
<description>Weight zero point. Must be zero for non-int8 types.</description>
<rank min="0" max="0"/>
</argument>
<argument category="attribute" name="local_bound" type="tensor_t" shape="-" tensor-element-type="bool_t" optional="true">
<description>
This optional attribute affects the floating-point compliance error bound.
The default of false allows for direct and transform based, fast convolution algorithms.
Only set to true if direct dot-product calculation precision is required.
</description>
<rank min="0" max="0"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="[N,OH,OW,OC]" tensor-element-type="out_t">
<description>Output tensor</description>
<rank min="4" max="4"/>
</argument>
</arguments>
<types>
<type name='in_t' />
<type name='weight_t' />
<type name='out_t' />
</types>
<typesupport mode="signed 8x8 with int32 accumulate" in_t="i8_t" weight_t="i8_t" out_t="i32_t" />
<typesupport mode="signed 8x4 with int32 accumulate" in_t="i8_t" weight_t="i4_t" out_t="i32_t" />
<typesupport mode="signed 16x8 with int48 accumulate" in_t="i16_t" weight_t="i8_t" out_t="i48_t" />
<typesupport mode="fp16 with fp16 accumulate" in_t="fp16_t" weight_t="fp16_t" out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp16 with fp32 accumulate" in_t="fp16_t" weight_t="fp16_t" out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16 with fp32 accumulate" in_t="bf16_t" weight_t="bf16_t" out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32 with fp32 accumulate" in_t="fp32_t" weight_t="fp32_t" out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
</operatorgroup>
<operatorgroup name="activation">
<operator>
<name>CLAMP</name>
<arguments>
<argument category="input" name="input" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Input tensor</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="attribute" name="min_val" type="tensor_t" shape="-" tensor-element-type="in_out_t">
<description>Minimum clip value</description>
<rank min="0" max="0"/>
</argument>
<argument category="attribute" name="max_val" type="tensor_t" shape="-" tensor-element-type="in_out_t">
<description>Maximum clip value</description>
<rank min="0" max="0"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor of same type and shape as input</description>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="signed 8" in_out_t="i8_t"/>
<typesupport mode="signed 16" in_out_t="i16_t"/>
<typesupport mode="fp16" in_out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>ERF</name>
<arguments>
<argument category="input" name="input" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Input tensor</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor of same type and shape as input</description>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="fp16" in_out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>SIGMOID</name>
<arguments>
<argument category="input" name="input" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Input tensor</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor of same type and shape as input</description>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="fp16" in_out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>TANH</name>
<arguments>
<argument category="input" name="input" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Input tensor</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor of same type and shape as input</description>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="fp16" in_out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
</operatorgroup>
<operatorgroup name="elementwise-binary">
<operator>
<name>ADD</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape1" tensor-element-type="in_out_t">
<description>Input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="input" name="input2" type="tensor_t" shape="shape2" tensor-element-type="in_out_t">
<description>Input tensor with the same rank as input1</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor with broadcast shape if necessary</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="signed 32" in_out_t="i32_t"/>
<typesupport mode="shape" in_out_t="shape_t"/>
<typesupport mode="fp16" in_out_t="fp16_t" >
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t" >
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t" >
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>ARITHMETIC_RIGHT_SHIFT</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape1" tensor-element-type="in_out_t">
<description>Input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="input" name="input2" type="tensor_t" shape="shape2" tensor-element-type="in_out_t">
<description>Input tensor with the same rank as input1</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="attribute" name="round" type="tensor_t" shape="-" tensor-element-type="bool_t">
<description>If true then the shift is rounded</description>
<rank min="0" max="0"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor with broadcast shape if necessary</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="signed 8" in_out_t="i8_t"/>
<typesupport mode="signed 16" in_out_t="i16_t"/>
<typesupport mode="signed 32" in_out_t="i32_t"/>
</operator>
<operator>
<name>BITWISE_AND</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape1" tensor-element-type="in_out_t">
<description>Input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="input" name="input2" type="tensor_t" shape="shape2" tensor-element-type="in_out_t">
<description>Input tensor with the same rank as input1</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor with broadcast shape if necessary</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="signed 8" in_out_t="i8_t"/>
<typesupport mode="signed 16" in_out_t="i16_t"/>
<typesupport mode="signed 32" in_out_t="i32_t"/>
</operator>
<operator>
<name>BITWISE_OR</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape1" tensor-element-type="in_out_t">
<description>Input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="input" name="input2" type="tensor_t" shape="shape2" tensor-element-type="in_out_t">
<description>Input tensor with the same rank as input1</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor with broadcast shape if necessary</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="signed 8" in_out_t="i8_t"/>
<typesupport mode="signed 16" in_out_t="i16_t"/>
<typesupport mode="signed 32" in_out_t="i32_t"/>
</operator>
<operator>
<name>BITWISE_XOR</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape1" tensor-element-type="in_out_t">
<description>Input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="input" name="input2" type="tensor_t" shape="shape2" tensor-element-type="in_out_t">
<description>Input tensor with the same rank as input1</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor with broadcast shape if necessary</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="signed 8" in_out_t="i8_t"/>
<typesupport mode="signed 16" in_out_t="i16_t"/>
<typesupport mode="signed 32" in_out_t="i32_t"/>
</operator>
<operator>
<name>INTDIV</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape1" tensor-element-type="in_out_t">
<description>Input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="input" name="input2" type="tensor_t" shape="shape2" tensor-element-type="in_out_t">
<description>Input tensor with the same rank as input1</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor with broadcast shape if necessary</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="signed 32" in_out_t="i32_t"/>
<typesupport mode="shape" in_out_t="shape_t"/>
</operator>
<operator>
<name>LOGICAL_AND</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape1" tensor-element-type="in_out_t">
<description>Input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="input" name="input2" type="tensor_t" shape="shape2" tensor-element-type="in_out_t">
<description>Input tensor with the same rank as input1</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor with broadcast shape if necessary</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="boolean" in_out_t="bool_t"/>
</operator>
<operator>
<name>LOGICAL_LEFT_SHIFT</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape1" tensor-element-type="in_out_t">
<description>Input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="input" name="input2" type="tensor_t" shape="shape2" tensor-element-type="in_out_t">
<description>Input tensor with the same rank as input1</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor with broadcast shape if necessary</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="signed 8" in_out_t="i8_t"/>
<typesupport mode="signed 16" in_out_t="i16_t"/>
<typesupport mode="signed 32" in_out_t="i32_t"/>
</operator>
<operator>
<name>LOGICAL_RIGHT_SHIFT</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape1" tensor-element-type="in_out_t">
<description>Input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="input" name="input2" type="tensor_t" shape="shape2" tensor-element-type="in_out_t">
<description>Input tensor with the same rank as input1</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor with broadcast shape if necessary</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="signed 8" in_out_t="i8_t"/>
<typesupport mode="signed 16" in_out_t="i16_t"/>
<typesupport mode="signed 32" in_out_t="i32_t"/>
</operator>
<operator>
<name>LOGICAL_OR</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape1" tensor-element-type="in_out_t">
<description>Input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="input" name="input2" type="tensor_t" shape="shape2" tensor-element-type="in_out_t">
<description>Input tensor with the same rank as input1</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor with broadcast shape if necessary</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="boolean" in_out_t="bool_t"/>
</operator>
<operator>
<name>LOGICAL_XOR</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape1" tensor-element-type="in_out_t">
<description>Input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="input" name="input2" type="tensor_t" shape="shape2" tensor-element-type="in_out_t">
<description>Input tensor with the same rank as input1</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor with broadcast shape if necessary</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="boolean" in_out_t="bool_t"/>
</operator>
<operator>
<name>MAXIMUM</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape1" tensor-element-type="in_out_t">
<description>Input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="input" name="input2" type="tensor_t" shape="shape2" tensor-element-type="in_out_t">
<description>Input tensor with the same rank as input1</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor with broadcast shape if necessary</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="signed 32" in_out_t="i32_t"/>
<typesupport mode="fp16" in_out_t="fp16_t" >
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t" >
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t" >
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>MINIMUM</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape1" tensor-element-type="in_out_t">
<description>Input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="input" name="input2" type="tensor_t" shape="shape2" tensor-element-type="in_out_t">
<description>Input tensor with the same rank as input1</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor with broadcast shape if necessary</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="signed 32" in_out_t="i32_t"/>
<typesupport mode="fp16" in_out_t="fp16_t" >
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t" >
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t" >
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>MUL</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape1" tensor-element-type="in_t">
<description>Input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="input" name="input2" type="tensor_t" shape="shape2" tensor-element-type="in_t">
<description>Input tensor with the same rank as input1</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="input(MT)|attribute(BI,MI)" name="shift" type="tensor_t" shape="-" tensor-element-type="i8_t">
<description>Result right shift (i32_t data type only)</description>
<rank min="0" max="0"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="out_t">
<description>Output tensor with broadcast shape if necessary</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_t'/>
<type name='out_t'/>
</types>
<typesupport mode="signed 8" in_t="i8_t" out_t="i32_t"/>
<typesupport mode="signed 16" in_t="i16_t" out_t="i32_t"/>
<typesupport mode="signed 32" in_t="i32_t" out_t="i32_t"/>
<typesupport mode="shape" in_t="shape_t" out_t="shape_t"/>
<typesupport mode="fp16" in_t="fp16_t" out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_t="bf16_t" out_t="bf16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_t="fp32_t" out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>POW</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape1" tensor-element-type="in_out_t">
<description>Input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="input" name="input2" type="tensor_t" shape="shape2" tensor-element-type="in_out_t">
<description>Input tensor with the same rank as input1</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor with broadcast shape if necessary</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="fp16" in_out_t="fp16_t" >
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t" >
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t" >
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>SUB</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape1" tensor-element-type="in_out_t">
<description>Input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="input" name="input2" type="tensor_t" shape="shape2" tensor-element-type="in_out_t">
<description>Input tensor with the same rank as input1</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor with broadcast shape if necessary</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="signed 32" in_out_t="i32_t"/>
<typesupport mode="shape" in_out_t="shape_t"/>
<typesupport mode="fp16" in_out_t="fp16_t" >
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t" >
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t" >
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>TABLE</name>
<arguments>
<argument category="input" name="input" type="tensor_t" shape="shape" tensor-element-type="in_t">
<description>Input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="input(MT)|attribute(BI,MI)" name="table" type="tensor_t" shape="[TABLE_SIZE]" tensor-element-type="table_t">
<description>Lookup table tensor</description>
<rank min="1" max="1"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="out_t">
<description>Output tensor</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_t'/>
<type name='table_t'/>
<type name='out_t'/>
<type name='TABLE_SIZE'/>
</types>
<typesupport mode="signed 8" in_t="i8_t" table_t="i8_t" TABLE_SIZE="256" out_t="i8_t"/>
<typesupport mode="signed 16" in_t="i16_t" table_t="i16_t" TABLE_SIZE="513" out_t="i32_t"/>
</operator>
</operatorgroup>
<operatorgroup name="elementwise-unary">
<operator>
<name>ABS</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Input tensor</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor of same type, size as the input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="signed 32" in_out_t="i32_t"/>
<typesupport mode="fp16" in_out_t="fp16_t" >
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t" >
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t" >
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>BITWISE_NOT</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Input tensor</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor of same type, size as the input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="signed 8" in_out_t="i8_t"/>
<typesupport mode="signed 16" in_out_t="i16_t"/>
<typesupport mode="signed 32" in_out_t="i32_t"/>
</operator>
<operator>
<name>CEIL</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Input tensor</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor of same type, size as the input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="fp16" in_out_t="fp16_t" >
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t" >
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t" >
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>CLZ</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Input tensor</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor of same type, size as the input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="signed 32" in_out_t="i32_t"/>
</operator>
<operator>
<name>EXP</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Input tensor</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor of same type, size as the input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="fp16" in_out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>FLOOR</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Input tensor</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor of same type, size as the input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="fp16" in_out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>LOG</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Input tensor</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor of same type, size as the input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="fp16" in_out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>LOGICAL_NOT</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Input tensor</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor of same type, size as the input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="Boolean" in_out_t="bool_t"/>
</operator>
<operator>
<name>NEGATE</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Input tensor</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="attribute" name="input1_zp" type="tensor_t" shape="-" tensor-element-type="in_out_t">
<description>Input 1 zero point. Must be zero for non-int8 types.</description>
<rank min="0" max="0"/>
</argument>
<argument category="attribute" name="output_zp" type="tensor_t" shape="-" tensor-element-type="in_out_t">
<description>Output zero point. Must be zero for non-int8 types.</description>
<rank min="0" max="0"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor of same type, size as the input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
<type name='acc_t'/>
</types>
<typesupport mode="signed 8" in_out_t="i8_t" acc_t="i32_t"/>
<typesupport mode="signed 16" in_out_t="i16_t" acc_t="i32_t"/>
<typesupport mode="signed 32" in_out_t="i32_t" acc_t="i32_t"/>
<typesupport mode="fp16" in_out_t="fp16_t" acc_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t" acc_t="bf16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t" acc_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>RECIPROCAL</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Input tensor</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor of same type, size as the input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="fp16" in_out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>RSQRT</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Input tensor</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor of same type, size as the input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="fp16" in_out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
</operatorgroup>
<operatorgroup name="elementwise-ternary">
<operator>
<name>SELECT</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape1" tensor-element-type="bool_t">
<description>Input selector tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="input" name="input2" type="tensor_t" shape="shape2" tensor-element-type="in_out_t">
<description>Input value tensor if input1 is True</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="input" name="input3" type="tensor_t" shape="shape3" tensor-element-type="in_out_t">
<description>Input value tensor if input1 is False</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor of same type as input2 and input3, with broadcast shape if necessary</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="Boolean" in_out_t="bool_t"/>
<typesupport mode="signed 8" in_out_t="i8_t"/>
<typesupport mode="signed 16" in_out_t="i16_t"/>
<typesupport mode="signed 32" in_out_t="i32_t"/>
<typesupport mode="fp16" in_out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
</operatorgroup>
<operatorgroup name="comparison">
<operator>
<name>EQUAL</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape1" tensor-element-type="in_t">
<description>Input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="input" name="input2" type="tensor_t" shape="shape2" tensor-element-type="in_t">
<description>Input tensor with the same rank as input1</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="out_t">
<description>Output tensor with broadcast shape if necessary</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_t'/>
<type name='out_t'/>
</types>
<typesupport mode="signed 32" in_t="i32_t" out_t="bool_t"/>
<typesupport mode="fp16" in_t="fp16_t" out_t="bool_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_t="bf16_t" out_t="bool_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_t="fp32_t" out_t="bool_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>GREATER</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape1" tensor-element-type="in_t">
<description>Input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="input" name="input2" type="tensor_t" shape="shape2" tensor-element-type="in_t">
<description>Input tensor with the same rank as input1</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="out_t">
<description>Output tensor with broadcast shape if necessary</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_t'/>
<type name='out_t'/>
</types>
<typesupport mode="signed 32" in_t="i32_t" out_t="bool_t"/>
<typesupport mode="fp16" in_t="fp16_t" out_t="bool_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_t="bf16_t" out_t="bool_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_t="fp32_t" out_t="bool_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>GREATER_EQUAL</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape1" tensor-element-type="in_t">
<description>Input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="input" name="input2" type="tensor_t" shape="shape2" tensor-element-type="in_t">
<description>Input tensor with the same rank as input1</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="out_t">
<description>Output tensor with broadcast shape if necessary</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_t'/>
<type name='out_t'/>
</types>
<typesupport mode="signed 32" in_t="i32_t" out_t="bool_t"/>
<typesupport mode="fp16" in_t="fp16_t" out_t="bool_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_t="bf16_t" out_t="bool_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_t="fp32_t" out_t="bool_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
</operatorgroup>
<operatorgroup name="reduction">
<operator>
<name>REDUCE_ALL</name>
<arguments>
<argument category="input" name="input" type="tensor_t" shape="shape1" tensor-element-type="in_out_t">
<description>Input tensor</description>
<rank min="1" max="MAX_RANK"/>
</argument>
<argument category="attribute" name="axis" type="tensor_t" shape="-" tensor-element-type="i32_t">
<description>Axis to reduce, in range from 0 to rank(shape1)-1</description>
<rank min="0" max="0"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor. Same rank as the input tensor.</description>
<rank min="1" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="boolean" in_out_t="bool_t"/>
</operator>
<operator>
<name>REDUCE_ANY</name>
<arguments>
<argument category="input" name="input" type="tensor_t" shape="shape1" tensor-element-type="in_out_t">
<description>Input tensor</description>
<rank min="1" max="MAX_RANK"/>
</argument>
<argument category="attribute" name="axis" type="tensor_t" shape="-" tensor-element-type="i32_t">
<description>Axis to reduce, in range from 0 to rank(shape1)-1</description>
<rank min="0" max="0"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor. Same rank as the input tensor.</description>
<rank min="1" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="boolean" in_out_t="bool_t"/>
</operator>
<operator>
<name>REDUCE_MAX</name>
<arguments>
<argument category="input" name="input" type="tensor_t" shape="shape1" tensor-element-type="in_out_t">
<description>Input tensor</description>
<rank min="1" max="MAX_RANK"/>
</argument>
<argument category="attribute" name="axis" type="tensor_t" shape="-" tensor-element-type="i32_t">
<description>Axis to reduce, in range from 0 to rank(shape1)-1</description>
<rank min="0" max="0"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor. Same rank as the input tensor.</description>
<rank min="1" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="signed 8" in_out_t="i8_t"/>
<typesupport mode="signed 16" in_out_t="i16_t"/>
<typesupport mode="signed 32" in_out_t="i32_t"/>
<typesupport mode="fp16" in_out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>REDUCE_MIN</name>
<arguments>
<argument category="input" name="input" type="tensor_t" shape="shape1" tensor-element-type="in_out_t">
<description>Input tensor</description>
<rank min="1" max="MAX_RANK"/>
</argument>
<argument category="attribute" name="axis" type="tensor_t" shape="-" tensor-element-type="i32_t">
<description>Axis to reduce, in range from 0 to rank(shape1)-1</description>
<rank min="0" max="0"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor. Same rank as the input tensor.</description>
<rank min="1" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="signed 8" in_out_t="i8_t"/>
<typesupport mode="signed 16" in_out_t="i16_t"/>
<typesupport mode="signed 32" in_out_t="i32_t"/>
<typesupport mode="fp16" in_out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>REDUCE_PRODUCT</name>
<arguments>
<argument category="input" name="input" type="tensor_t" shape="shape1" tensor-element-type="in_out_t">
<description>Input tensor</description>
<rank min="1" max="MAX_RANK"/>
</argument>
<argument category="attribute" name="axis" type="tensor_t" shape="-" tensor-element-type="i32_t">
<description>Axis to reduce, in range from 0 to rank(shape1)-1</description>
<rank min="0" max="0"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor. Same rank as the input tensor.</description>
<rank min="1" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="fp16" in_out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>REDUCE_SUM</name>
<arguments>
<argument category="input" name="input" type="tensor_t" shape="shape1" tensor-element-type="in_out_t">
<description>Input tensor with rank from 1 to 4</description>
<rank min="1" max="MAX_RANK"/>
</argument>
<argument category="attribute" name="axis" type="tensor_t" shape="-" tensor-element-type="i32_t">
<description>Axis to reduce, in range from 0 to rank(shape1)-1</description>
<rank min="0" max="0"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor. Same rank as the input tensor.</description>
<rank min="1" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="signed 32" in_out_t="i32_t"/>
<typesupport mode="fp16" in_out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
</operatorgroup>
<operatorgroup name="data-layout">
<operator>
<name>CONCAT</name>
<arguments>
<argument category="input" name="input1" type="tensor_list_t" shape="shapes1" tensor-element-type="in_out_t">
<description>List of input tensors. All inputs must have the same rank and data type</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="attribute" name="axis" type="tensor_t" shape="-" tensor-element-type="i32_t">
<description>Axis along which concatenation is to occur, in range from 0 to rank(shape)-1</description>
<rank min="0" max="0"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="1" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="boolean" in_out_t="bool_t"/>
<typesupport mode="signed 8" in_out_t="i8_t"/>
<typesupport mode="signed 16" in_out_t="i16_t"/>
<typesupport mode="signed 32" in_out_t="i32_t"/>
<typesupport mode="shape" in_out_t="shape_t"/>
<typesupport mode="fp16" in_out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>PAD</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape1" tensor-element-type="in_out_t">
<description>Input tensor</description>
<rank min="1" max="MAX_RANK"/>
</argument>
<argument category="input" name="padding" type="tensor_t" shape="[rank(shape1),2]" tensor-element-type="shape_t">
<description>Number of pad elements at the start and end of each dimension</description>
<rank min="2" max="2"/>
</argument>
<argument category="attribute" name="pad_const" type="tensor_t" shape="-" tensor-element-type="in_out_t">
<description>Constant value to be used as padding</description>
<rank min="0" max="0"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor of same type as the input tensor</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="1" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="boolean" in_out_t="bool_t"/>
<typesupport mode="signed 8" in_out_t="i8_t"/>
<typesupport mode="signed 16" in_out_t="i16_t"/>
<typesupport mode="signed 32" in_out_t="i32_t"/>
<typesupport mode="fp16" in_out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>DIM</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape" tensor-element-type="in_t">
<description>Input tensor</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="1" max="MAX_RANK"/>
</argument>
<argument category="attribute" name="axis" type="tensor_t" shape="-" tensor-element-type="i32_t">
<description>Axis in range from 0 to rank(shape) - 1</description>
<rank min="0" max="0"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="-" tensor-element-type="shape_t" >
<description>Output rank 0 tensor giving the size of the shape for the given axis</description>
<rank min="0" max="0"/>
</argument>
</arguments>
<types>
<type name='in_t'/>
</types>
<typesupport mode="boolean" in_t="bool_t"/>
<typesupport mode="signed 8" in_t="i8_t"/>
<typesupport mode="signed 16" in_t="i16_t"/>
<typesupport mode="signed 32" in_t="i32_t"/>
<typesupport mode="fp16" in_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_t="bf16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>RESHAPE</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape1" tensor-element-type="in_out_t">
<description>Input tensor</description>
<levellimit value="rank(shape1)" limit="MAX_RANK"/>
<rank min="1" max="MAX_RANK"/>
</argument>
<argument category="input" name="shape" type="tensor_t" shape="[rank(shape)]" tensor-element-type="shape_t">
<description>1D shape tensor giving the new shape.</description>
<rank min="1" max="1"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor of same type, size as the input tensor</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="1" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="boolean" in_out_t="bool_t"/>
<typesupport mode="signed 8" in_out_t="i8_t"/>
<typesupport mode="signed 16" in_out_t="i16_t"/>
<typesupport mode="signed 32" in_out_t="i32_t"/>
<typesupport mode="fp16" in_out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>REVERSE</name>
<arguments>
<argument category="input" name="input" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Input tensor</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="1" max="MAX_RANK"/>
</argument>
<argument category="attribute" name="axis" type="tensor_t" shape="-" tensor-element-type="i32_t">
<description>Axis to reverse, in range from 0 to rank(shape)-1</description>
<rank min="0" max="0"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor. Same shape as input tensor</description>
<rank min="1" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="boolean" in_out_t="bool_t"/>
<typesupport mode="signed 8" in_out_t="i8_t"/>
<typesupport mode="signed 16" in_out_t="i16_t"/>
<typesupport mode="signed 32" in_out_t="i32_t"/>
<typesupport mode="shape" in_out_t="shape_t"/>
<typesupport mode="fp16" in_out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>SLICE</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape1" tensor-element-type="in_out_t">
<description>Input tensor</description>
<rank min="1" max="MAX_RANK"/>
</argument>
<argument category="attribute" name="start" type="tensor_t" shape="[rank(shape1)]" tensor-element-type="index_t">
<description>List of integer coordinates, of length equal to the rank of input1. Start coordinate for slicing.</description>
<rank min="1" max="1"/>
</argument>
<argument category="attribute" name="size" type="tensor_t" shape="[rank(shape1)]" tensor-element-type="index_t">
<description>List of integer size values, of length equal to the rank of input1. Size of the input to be
used.</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="1" max="1"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor of same type as the input tensor</description>
<rank min="1" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="boolean" in_out_t="bool_t"/>
<typesupport mode="signed 8" in_out_t="i8_t"/>
<typesupport mode="signed 16" in_out_t="i16_t"/>
<typesupport mode="signed 32" in_out_t="i32_t"/>
<typesupport mode="fp16" in_out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>TILE</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape1" tensor-element-type="in_out_t">
<description>Input tensor</description>
<rank min="1" max="MAX_RANK"/>
</argument>
<argument category="input" name="multiples" type="tensor_t" shape="[rank(shape1)]" tensor-element-type="shape_t">
<description>Number of times to replicate input1 in each dimension</description>
<rank min="1" max="1"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor of same type, rank as the input tensor</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="1" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="boolean" in_out_t="bool_t"/>
<typesupport mode="signed 8" in_out_t="i8_t"/>
<typesupport mode="signed 16" in_out_t="i16_t"/>
<typesupport mode="signed 32" in_out_t="i32_t"/>
<typesupport mode="fp16" in_out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>TRANSPOSE</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape1" tensor-element-type="in_out_t">
<description>Input tensor</description>
<rank min="1" max="MAX_RANK"/>
</argument>
<argument category="attribute" name="perms" type="tensor_t" shape="[rank(shape1)]" tensor-element-type="i32_t">
<description>List of integers of length equal to the rank of input1. Values must be valid dimensions within shape1, and may not be repeated.</description>
<rank min="1" max="1"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor of same type, rank as the input tensor</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="1" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="boolean" in_out_t="bool_t"/>
<typesupport mode="signed 8" in_out_t="i8_t"/>
<typesupport mode="signed 16" in_out_t="i16_t"/>
<typesupport mode="signed 32" in_out_t="i32_t"/>
<typesupport mode="fp16" in_out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
</operatorgroup>
<operatorgroup name="scatter-gather">
<operator>
<name>GATHER</name>
<arguments>
<argument category="input" name="values" type="tensor_t" shape="[N,K,C]" tensor-element-type="in_out_t">
<description>3D value tensor</description>
<rank min="3" max="3"/>
</argument>
<argument category="input" name="indices" type="tensor_t" shape="[N,W]" tensor-element-type="index_t">
<description>2D index tensor</description>
<rank min="2" max="2"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="[N,W,C]" tensor-element-type="in_out_t">
<description>3D output tensor</description>
<rank min="3" max="3"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="signed 8" in_out_t="i8_t"/>
<typesupport mode="signed 16" in_out_t="i16_t"/>
<typesupport mode="signed 32" in_out_t="i32_t"/>
<typesupport mode="fp16" in_out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>SCATTER</name>
<arguments>
<argument category="input" name="values_in" type="tensor_t" shape="[N,K,C]" tensor-element-type="in_out_t">
<description>3D values in tensor</description>
<rank min="3" max="3"/>
</argument>
<argument category="input" name="indices" type="tensor_t" shape="[N,W]" tensor-element-type="index_t">
<description>2D index tensor</description>
<rank min="2" max="2"/>
</argument>
<argument category="input" name="input" type="tensor_t" shape="[N,W,C]" tensor-element-type="in_out_t">
<description>3D input tensor</description>
<rank min="3" max="3"/>
</argument>
<argument category="output" name="values_out" type="tensor_t" shape="[N,K,C]" tensor-element-type="in_out_t">
<description>3D output tensor</description>
<rank min="3" max="3"/>
</argument>
</arguments>
<types>
<type name='in_out_t'/>
</types>
<typesupport mode="signed 8" in_out_t="i8_t"/>
<typesupport mode="signed 16" in_out_t="i16_t"/>
<typesupport mode="signed 32" in_out_t="i32_t"/>
<typesupport mode="fp16" in_out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
</operatorgroup>
<operatorgroup name="image">
<operator>
<name>RESIZE</name>
<arguments>
<argument category="input" name="input" type="tensor_t" shape="[N,IH,IW,C]" tensor-element-type="in_t">
<description>Input tensor</description>
<rank min="4" max="4"/>
</argument>
<argument category="input" name="scale" type="tensor_t" shape="[4]" tensor-element-type="shape_t">
<description>[scale_y_n, scale_y_d, scale_x_n, scale_x_d]</description>
<levellimit value="scale_y_n/scale_y_d" limit="MAX_SCALE"/>
<levellimit value="scale_x_n/scale_x_d" limit="MAX_SCALE"/>
<rank min="1" max="1"/>
</argument>
<argument category="input" name="offset" type="tensor_t" shape="[2]" tensor-element-type="shape_t">
<description>[offset_y, offset_x]</description>
<rank min="1" max="1"/>
</argument>
<argument category="input" name="border" type="tensor_t" shape="[2]" tensor-element-type="shape_t">
<description>[border_y, border_x]</description>
<rank min="1" max="1"/>
</argument>
<argument category="attribute" name="mode" type="tensor_t" shape="-" tensor-element-type="resize_mode_t">
<description>BILINEAR or NEAREST</description>
<rank min="0" max="0"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="[N,OH,OW,C]" tensor-element-type="out_t">
<description>Output tensor</description>
<rank min="4" max="4"/>
</argument>
</arguments>
<types>
<type name='resize_t'/>
<type name='in_t'/>
<type name='out_t'/>
</types>
<typesupport mode="signed 8, bilinear" resize_t="i16_t" in_t="i8_t" out_t="i32_t"/>
<typesupport mode="signed 8, nearest" resize_t="i16_t" in_t="i8_t" out_t="i8_t"/>
<typesupport mode="signed 16, bilinear" resize_t="i16_t" in_t="i16_t" out_t="i48_t"/>
<typesupport mode="signed 16, nearest" resize_t="i16_t" in_t="i16_t" out_t="i16_t"/>
<typesupport mode="fp16" resize_t="fp16_t" in_t="fp16_t" out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" resize_t="bf16_t" in_t="bf16_t" out_t="bf16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" resize_t="fp32_t" in_t="fp32_t" out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
</operatorgroup>
<operatorgroup name="type-conversion">
<operator>
<name>CAST</name>
<arguments>
<argument category="input" name="input" type="tensor_t" shape="shape" tensor-element-type="in_t">
<description>Input tensor</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="out_t">
<description>Output tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_t'/>
<type name='out_t'/>
</types>
<typesupport mode="bool to signed 8" in_t="bool_t" out_t="i8_t"/>
<typesupport mode="bool to signed 16" in_t="bool_t" out_t="i16_t"/>
<typesupport mode="bool to signed 32" in_t="bool_t" out_t="i32_t"/>
<typesupport mode="signed 8 to bool" in_t="i8_t" out_t="bool_t"/>
<typesupport mode="signed 8 to signed 16" in_t="i8_t" out_t="i16_t"/>
<typesupport mode="signed 8 to signed 32" in_t="i8_t" out_t="i32_t"/>
<typesupport mode="signed 8 to fp16" in_t="i8_t" out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="signed 8 to bf16" in_t="i8_t" out_t="bf16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="signed 8 to fp32" in_t="i8_t" out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="signed 16 to bool" in_t="i16_t" out_t="bool_t"/>
<typesupport mode="signed 16 to signed 8" in_t="i16_t" out_t="i8_t"/>
<typesupport mode="signed 16 to signed 32" in_t="i16_t" out_t="i32_t"/>
<typesupport mode="signed 16 to fp16" in_t="i16_t" out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="signed 16 to bf16" in_t="i16_t" out_t="bf16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="signed 16 to fp32" in_t="i16_t" out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="signed 32 to bool" in_t="i32_t" out_t="bool_t"/>
<typesupport mode="signed 32 to signed 8" in_t="i32_t" out_t="i8_t"/>
<typesupport mode="signed 32 to signed 16" in_t="i32_t" out_t="i16_t"/>
<typesupport mode="signed 32 to fp16" in_t="i32_t" out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="signed 32 to bf16" in_t="i32_t" out_t="bf16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="signed 32 to fp32" in_t="i32_t" out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16 to signed 8" in_t="bf16_t" out_t="i8_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16 to signed 16" in_t="bf16_t" out_t="i16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16 to signed 32" in_t="bf16_t" out_t="i32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16 to fp32" in_t="bf16_t" out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp16 to signed 8" in_t="fp16_t" out_t="i8_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp16 to signed 16" in_t="fp16_t" out_t="i16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp16 to signed 32" in_t="fp16_t" out_t="i32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp16 to fp32" in_t="fp16_t" out_t="fp32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32 to signed 8" in_t="fp32_t" out_t="i8_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32 to signed 16" in_t="fp32_t" out_t="i16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32 to signed 32" in_t="fp32_t" out_t="i32_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32 to bf16" in_t="fp32_t" out_t="bf16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32 to fp16" in_t="fp32_t" out_t="fp16_t">
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>RESCALE</name>
<arguments>
<argument category="input" name="input" type="tensor_t" shape="shape" tensor-element-type="in_t">
<description>Input tensor</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="out_t">
<description>Output tensor with the same shape as input</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="attribute" name="input_zp" type="tensor_t" shape="-" tensor-element-type="in_t">
<description>Input tensor zero point. int8/uint8 can have zero point within their valid range. uint16 zero point must be either 0 or 32768. All other types must have zero point equal to 0.</description>
<rank min="0" max="0"/>
</argument>
<argument category="attribute" name="output_zp" type="tensor_t" shape="-" tensor-element-type="out_t">
<description>Output tensor zero point.int8/uint8 can have zero point within their valid range. uint16 zero point must be either 0 or 32768. All other types must have zero point equal to 0.</description>
<rank min="0" max="0"/>
</argument>
<argument category="input(MT)|attribute(BI,MI)" name="multiplier" type="tensor_t" shape="[NC]" tensor-element-type="mul_t">
<description>Scaling multiplier array</description>
<rank min="1" max="1"/>
</argument>
<argument category="input(MT)|attribute(BI,MI)" name="shift" type="tensor_t" shape="[NC]" tensor-element-type="i8_t">
<description>Scaling shift array</description>
<rank min="1" max="1"/>
</argument>
<argument category="attribute" name="scale32" type="tensor_t" shape="-" tensor-element-type="bool_t">
<description>if (scale32) mul_t=i32_t else mul_t=i16_t</description>
<rank min="0" max="0"/>
</argument>
<argument category="attribute" name="double_round" type="tensor_t" shape="-" tensor-element-type="bool_t">
<description>Select double round mode</description>
<rank min="0" max="0"/>
</argument>
<argument category="attribute" name="per_channel" type="tensor_t" shape="-" tensor-element-type="bool_t">
<description>if (per_channel) NC=shape[rank(shape)-1] else NC=1</description>
<rank min="0" max="0"/>
</argument>
<argument category="attribute" name="input_unsigned" type="tensor_t" shape="-" tensor-element-type="bool_t">
<description>If True, treat the input values as unsigned.</description>
<rank min="0" max="0"/>
</argument>
<argument category="attribute" name="output_unsigned" type="tensor_t" shape="-" tensor-element-type="bool_t">
<description>If True, treat the output values as unsigned.</description>
<rank min="0" max="0"/>
</argument>
</arguments>
<types>
<type name='in_t'/>
<type name='out_t'/>
</types>
<typesupport mode="8-bit to 8-bit" in_t="i8_t" out_t="i8_t"/>
<typesupport mode="8-bit to 16-bit" in_t="i8_t" out_t="i16_t"/>
<typesupport mode="8-bit to 32-bit" in_t="i8_t" out_t="i32_t"/>
<typesupport mode="16-bit to 8-bit" in_t="i16_t" out_t="i8_t"/>
<typesupport mode="16-bit to 16-bit" in_t="i16_t" out_t="i16_t"/>
<typesupport mode="16-bit to 32-bit" in_t="i16_t" out_t="i32_t"/>
<typesupport mode="32-bit to 8-bit" in_t="i32_t" out_t="i8_t"/>
<typesupport mode="32-bit to 16-bit" in_t="i32_t" out_t="i16_t"/>
<typesupport mode="32-bit to 32-bit" in_t="i32_t" out_t="i32_t"/>
<typesupport mode="48-bit to 8-bit" in_t="i48_t" out_t="i8_t"/>
<typesupport mode="48-bit to 16-bit" in_t="i48_t" out_t="i16_t"/>
<typesupport mode="48-bit to 32-bit" in_t="i48_t" out_t="i32_t"/>
</operator>
</operatorgroup>
<operatorgroup name="data-node">
<operator>
<name>CONST</name>
<arguments>
<argument category="attribute" name="values" type="tensor_t" shape="shape" tensor-element-type="out_t">
<description>Constant values</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="out_t">
<description>Output tensor of the same type, size as the input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='out_t' />
</types>
<typesupport mode="Boolean" out_t="bool_t" />
<typesupport mode="4-bit" out_t="i4_t" />
<typesupport mode="8-bit" out_t="i8_t" />
<typesupport mode="16-bit" out_t="i16_t" />
<typesupport mode="32-bit" out_t="i32_t" />
<typesupport mode="48-bit" out_t="i48_t" />
<typesupport mode="shape" out_t="shape_t" />
<typesupport mode="fp16" out_t="fp16_t" >
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" out_t="bf16_t" >
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" out_t="fp32_t" >
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
<operator>
<name>IDENTITY</name>
<arguments>
<argument category="input" name="input1" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
<argument category="output" name="output" type="tensor_t" shape="shape" tensor-element-type="in_out_t">
<description>Output tensor of the same type, size as the input tensor</description>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
<types>
<type name='in_out_t' />
</types>
<typesupport mode="Boolean" in_out_t="bool_t" />
<typesupport mode="8-bit" in_out_t="i8_t" />
<typesupport mode="16-bit" in_out_t="i16_t" />
<typesupport mode="32-bit" in_out_t="i32_t" />
<typesupport mode="fp16" in_out_t="fp16_t" >
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="bf16" in_out_t="bf16_t" >
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
<typesupport mode="fp32" in_out_t="fp32_t" >
<profile name="MI"/>
<profile name="MT"/>
</typesupport>
</operator>
</operatorgroup>
<operatorgroup name="custom">
<operator>
<name>CUSTOM</name>
<arguments>
<argument category="input" name="input_list" type="tensor_list_t" shape="-" tensor-element-type="-">
<description>List of input tensors</description>
</argument>
<argument category="attribute" name="operator" type="String" shape="-" tensor-element-type="-">
<description>String which tells the backend which custom operator is being called</description>
</argument>
<argument category="attribute" name="domain" type="String" shape="-" tensor-element-type="-">
<description>String idenifier which can help avoid name collisions on the operator field.
Different implementations of a given operator would be in different domains.
Implementations can choose which domains they want to support.</description>
</argument>
<argument category="attribute" name="implementation_attrs" type="String" shape="-" tensor-element-type="-">
<description>String value containing implementation specific attributes which apply to the operation</description>
</argument>
<argument category="output" name="output_list" type="tensor_list_t" shape="-" tensor-element-type="-">
<description>List of output tensors</description>
</argument>
</arguments>
</operator>
</operatorgroup>
<operatorgroup name="control-flow">
<operator>
<name>COND_IF</name>
<arguments>
<argument category="input" name="condition" type="tensor_t" shape="shape" tensor-element-type="bool_t">
<description>Input condition as a size 1 tensor</description>
<rank min="1" max="MAX_RANK"/>
</argument>
<argument category="input" name="input_list" type="tensor_list_t" shape="-" tensor-element-type="-">
<description>List of input tensors</description>
</argument>
<argument category="attribute" name="then_graph" type="tosa_graph_t" shape="-" tensor-element-type="-">
<description>TOSA graph to execute if condition is true</description>
</argument>
<argument category="attribute" name="else_graph" type="tosa_graph_t" shape="-" tensor-element-type="-">
<description>TOSA graph to execute if condition is false</description>
</argument>
<argument category="output" name="output_list" type="tensor_list_t" shape="-" tensor-element-type="-">
<description>List of output tensors</description>
</argument>
</arguments>
</operator>
<operator>
<name>WHILE_LOOP</name>
<arguments>
<argument category="input" name="input_list" type="tensor_list_t" shape="-" tensor-element-type="-">
<description>List of input tensors</description>
</argument>
<argument category="attribute" name="cond_graph" type="tosa_graph_t" shape="-" tensor-element-type="-">
<description>TOSA graph to evaluate the condition</description>
</argument>
<argument category="attribute" name="body_graph" type="tosa_graph_t" shape="-" tensor-element-type="-">
<description>TOSA graph to execute the loop body</description>
</argument>
<argument category="output" name="output_list" type="tensor_list_t" shape="-" tensor-element-type="-">
<description>List of output tensors</description>
</argument>
</arguments>
</operator>
</operatorgroup>
<operatorgroup name="variable">
<operator>
<name>VARIABLE</name>
<arguments>
<argument category="attribute" name="uid" type="tensor_t" shape="-" tensor-element-type="i32_t">
<description>Globally unique identifier for the declared variable tensor.</description>
<rank min="0" max="0"/>
</argument>
<argument category="attribute" name="var_shape" type="tensor_t" shape="var_shape" tensor-element-type="index_t">
<description>The variable tensor shape</description>
<rank min="1" max="1"/>
</argument>
<argument category="attribute" name="type" type="tensor_t" shape="-" tensor-element-type="var_t">
<description>Type of the tensor variable elements.</description>
<rank min="0" max="0"/>
</argument>
<argument category="attribute" name="initial_value" type="tensor_t" shape="shape" tensor-element-type="in_t" optional="true">
<description>Initial value of the variable tensor. This argument is optional with default value NULL.</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
</operator>
<operator>
<name>VARIABLE_WRITE</name>
<arguments>
<argument category="attribute" name="uid" type="tensor_t" shape="-" tensor-element-type="i32_t">
<description>Globally unique identifier of the variable tensor that is writing to</description>
<rank min="0" max="0"/>
</argument>
<argument category="input" name="input1" type="tensor_t" shape="shape" tensor-element-type="in_t">
<description>Input tensor</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
</operator>
<operator>
<name>VARIABLE_READ</name>
<arguments>
<argument category="attribute" name="uid" type="tensor_t" shape="-" tensor-element-type="i32_t">
<description>Globally unique identifier of the variable tensor that is reading from </description>
<rank min="0" max="0"/>
</argument>
<argument category="output" name="output1" type="tensor_t" shape="shape" tensor-element-type="out_t">
<description>Output tensor</description>
<levellimit value="rank(shape)" limit="MAX_RANK"/>
<rank min="0" max="MAX_RANK"/>
</argument>
</arguments>
</operator>
</operatorgroup>
</operators>
<enum name="resize_mode_t" description="Valid resize types">
<enumval value="0" name="NEAREST_NEIGHBOR" description="Nearest neighbor resize"/>
<enumval value="1" name="BILINEAR" description="Bilinear resize"/>
</enum>
<enum name="acc_size_t" description="Allowed accumulator sizes">
<enumval value="0" name="INT32" description="32-bit integer"/>
<enumval value="1" name="FP16" description="16-bit floating-point"/>
<enumval value="2" name="FP32" description="32-bit floating-point"/>
</enum>
<enum name="var_t" description="Variable tensor data type">
<enumval value="0" name="BOOLEAN" description="Boolean"/>
<enumval value="1" name="INT8" description="8-bit integer"/>
<enumval value="2" name="INT16" description="16-bit integer"/>
<enumval value="3" name="INT32" description="32-bit integer"/>
<enumval value="4" name="FP16" description="16-bit floating-point"/>
<enumval value="5" name="BF16" description="16-bit brain floating-point"/>
<enumval value="6" name="FP32" description="32-bit floating-point"/>
</enum>
</tosa>