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|
//
// This confidential and proprietary software may be used only as
// authorised by a licensing agreement from ARM Limited
// (C) COPYRIGHT 2020-2022 ARM Limited
// ALL RIGHTS RESERVED
// The entire notice above must be reproduced on all authorised
// copies and copies may only be made to the extent permitted
// by a licensing agreement from ARM Limited.
=== Elementwise Unary Operators
==== ABS
Elementwise absolute value operation
include::{generated}/operators/ABS.adoc[]
*Floating-point behavior:*
|===
|Input|-infinity|+infinity|-0|+0|NaN
|Output|+infinity|+infinity|+0|+0|NaN
|===
[source,c++]
----
for_each(index in shape) {
in_out_t value1 = tensor_read<in_out_t>(input1, shape, index);
if (in_out_t == float_t && value1 == -0.0) {
value1 = 0.0;
}
if (value1 < 0.0)
value1 = apply_sub<in_out_t>(0, value1);
tensor_write<in_out_t>(output, shape, index, value1);
}
----
==== BITWISE_NOT
Elementwise bitwise NOT of input tensor.
include::{generated}/operators/BITWISE_NOT.adoc[]
[source,c++]
----
for_each(index in shape) {
in_out_t value1 = tensor_read<in_out_t>(input1, shape, index);
in_out_t result = ~value1;
tensor_write<in_out_t>(output, shape, index, result);
}
----
==== CEIL
Elementwise ceiling operation
include::{generated}/operators/CEIL.adoc[]
*Floating-point behavior:*
|===
|Input|-infinity|+infinity|-0|+0|NaN
|Output|-infinity|+infinity|-0|+0|NaN
|===
[source,c++]
----
for_each(index in shape) {
in_out_t value1 = tensor_read<in_out_t>(input1, shape, index);
in_out_t result = apply_ceil<in_out_t>(value1);
tensor_write<in_out_t>(output, shape, index, result);
}
----
==== CLZ
Elementwise count leading zeros operation
include::{generated}/operators/CLZ.adoc[]
[source,c++]
----
for_each(index in shape) {
in_out_t value1 = tensor_read<in_out_t>(input1, shape, index);
in_out_t result = count_leading_zeros(value1);
tensor_write<in_out_t>(output, shape, index, result);
}
----
==== EXP
Elementwise e to the x operation
include::{generated}/operators/EXP.adoc[]
*Floating-point behavior:*
|===
|Input|-infinity|+infinity|-0|+0|NaN
|Output|+0|+infinity|1|1|NaN
|===
[source,c++]
----
for_each(index in shape) {
in_out_t value1 = tensor_read<in_out_t>(input1, shape, index);
in_out_t result = apply_exp<in_out_t>(value1);
tensor_write<in_out_t>(output, shape, index, result);
}
----
==== FLOOR
Elementwise floor operation
include::{generated}/operators/FLOOR.adoc[]
*Floating-point behavior:*
|===
|Input|-infinity|+infinity|-0|+0|NaN
|Output|-infinity|+infinity|-0|+0|NaN
|===
[source,c++]
----
for_each(index in shape) {
in_out_t value1 = tensor_read<in_out_t>(input1, shape, index);
in_out_t result = apply_floor<in_out_t>(value1);
tensor_write<in_out_t>(output, shape, index, result);
}
----
==== LOG
Elementwise natural logarithm operation
include::{generated}/operators/LOG.adoc[]
*Floating-point behavior:*
|===
|Input|-infinity|+infinity|-0|+0|NaN
|Output|NaN|+infinity|-infinity|-infinity|NaN
|===
[source,c++]
----
for_each(index in shape) {
in_out_t value1 = tensor_read<in_out_t>(input1, shape, index);
in_out_t result = apply_log<in_out_t>(value1);
tensor_write<in_out_t>(output, shape, index, result);
}
----
==== LOGICAL_NOT
Elementwise logical NOT of input.
include::{generated}/operators/LOGICAL_NOT.adoc[]
[source,c++]
----
for_each(index in shape) {
in_out_t value1 = tensor_read<in_out_t>(input1, shape1, index);
in_out_t result = !value1;
tensor_write<in_out_t>(output, shape, index, result);
}
----
==== NEGATE
Elementwise negation operation
include::{generated}/operators/NEGATE.adoc[]
*Floating-point behavior:*
|===
|Input|-infinity|+infinity|-0|+0|NaN
|Output|+infinity|-infinity|+0|-0|NaN
|===
[source,c++]
----
ERROR_IF(in_out_t != int8_t && input1_zp != 0) // Zero point only for int8_t
ERROR_IF(in_out_t != int8_t && output_zp != 0) // Zero point only for int8_t
for_each(index in shape) {
in_out_t value1 = tensor_read<in_out_t>(input1, shape, index);
acc_t value = (acc_t)value1 - input1_zp;
value = apply_sub<acc_t>(0, value);
in_out_t result = (in_out_t)apply_clip<acc_t>(value + output_zp, minimum<in_out_t>, maximum<in_out_t>);
tensor_write<in_out_t>(output, shape, index, result);
}
----
==== RECIPROCAL
Elementwise reciprocal operation. For integer operation, a TABLE should be used with the appropriate ranges.
include::{generated}/operators/RECIPROCAL.adoc[]
*Floating-point behavior:*
|===
|Input|-infinity|+infinity|-0|+0|NaN
|Output|-0|+0|-infinity|+infinity|NaN
|===
[source,c++]
----
for_each(index in shape) {
in_out_t value1 = tensor_read<in_out_t>(input1, shape1, index);
in_out_t result = 1.0 / value1;
tensor_write<in_out_t>(output, shape, index, result);
}
----
==== RSQRT
Elementwise reciprocal square root operation. For integer operation, a TABLE should be used with the appropriate ranges.
include::{generated}/operators/RSQRT.adoc[]
*Floating-point behavior:*
|===
|Input|-infinity|+infinity|-0|+0|NaN
|Output|NaN|+0|-infinity|+infinity|NaN
|===
[source,c++]
----
for_each(index in shape) {
in_out_t value1 = tensor_read<in_out_t>(input1, shape1, index);
in_out_t result;
if (value1 < 0) {
result = NaN;
}
else {
result = 1.0 / apply_sqrt<in_out_t>(value1);
}
tensor_write<in_out_t>(output, shape, index, result);
}
----
|
