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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 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
*Arguments:*
|===
|Argument|Type|Name|Shape|Description
|Input|in_t*|input1|shape|Input tensor
|Output|in_t*|output|shape|Output tensor of same type, size as the input tensor
|===
*Operation Function:*
[source,c]
----
for_each (index in shape) {
in_t value1 = tensor_read<in_t>(input1, shape, index)
if (value1 < 0)
value1 = apply_sub<in_t>(0, value1)
tensor_write<in_t>(output, shape, index, value1)
}
----
*Supported Data Types:*
|===
|Profile|Mode|in_t|out_t
|Any|signed 32|int32
|MI, MT|float|float
|===
==== BITWISE_NOT
Elementwise bitwise NOT of input tensor.
*Arguments:*
|===
|Argument|Type|Name|Shape|Description
|Input|in_t*|input1|shape|Input tensor
|Output|in_t*|output|shape|Output tensor of same type, size as the input tensor
|===
*Quantization Parameters:*
None
*Operation Function:*
[source,c]
----
for_each (index in shape) {
in_t value1 = tensor_read<in_t>(input1, shape, index)
in_t acc = ~value1
tensor_write<in_t>(output, shape, index, acc)
}
----
*Supported Data Types:*
|===
|Profile|Mode|in_t
|Any|signed 8|aint8
|Any|signed 16|int16
|Any|signed 32|int32
|===
==== CEIL
Elementwise ceiling operation
*Arguments:*
|===
|Argument|Type|Name|Shape|Description
|Input|in_t*|input1|shape|Input tensor
|Output|in_t*|output|shape|Output tensor of same type, size as the input tensor
|===
*Supported Data Types:*
|===
|Profile|Mode|in_t
|MI, MT|float|float
|===
==== CLZ
Elementwise count leading zeros operation
*Arguments:*
|===
|Argument|Type|Name|Shape|Description
|Input|in_t*|input1|shape|Input tensor
|Output|in_t*|output|shape|Output tensor of same type, size as the input tensor
|===
*Operation Function:*
[source,c]
----
for_each (index in shape) {
in_t acc = 0
in_t value1 = tensor_read<in_t>(input1, shape, index)
if (value1 == 0) {
acc = 32 // input1_width
}
else {
mask = 1 << (32 - 1) // input1_width - 1
acc = 0
while (mask & value1 == 0) {
mask = mask >> 1
acc = acc + 1
}
}
tensor_write<in_t>(output, shape, index, acc)
}
----
*Supported Data Types:*
|===
|Profile|Mode|in_t
|Any|signed 32|int32
|===
==== EXP
Elementwise e to the x operation
*Arguments:*
|===
|Argument|Type|Name|Shape|Description
|Input|in_t*|input1|shape|Input tensor
|Output|in_t*|output|shape|Output tensor of same type, size as the input tensor
|===
*Supported Data Types:*
|===
|Profile|Mode|in_t
|Any|float|float
|===
==== FLOOR
Elementwise floor operation
*Arguments:*
|===
|Argument|Type|Name|Shape|Description
|Input|in_t*|input1|shape|Input tensor
|Output|in_t*|output|shape|Output tensor of same type, size as the input tensor
|===
*Supported Data Types:*
|===
|Profile|Mode|in_t
|MI, MT|float|float
|===
==== LOG
Elementwise natural logarithm operation
*Arguments:*
|===
|Argument|Type|Name|Shape|Description
|Input|in_t*|input1|shape|Input tensor
|Output|in_t*|output|shape|Output tensor of same type, size as the input tensor
|===
*Supported Data Types:*
|===
|Profile|Mode|in_t
|MI, MT|float|float
|===
==== LOGICAL_NOT
Elementwise logical NOT of input.
*Arguments:*
|===
|Argument|Type|Name|Shape|Description
|Input|in_t*|input1|shape|Input tensor
|Output|in_t*|output|shape|Output tensor of same type, size as the input tensor
|===
*Quantization Parameters:*
None
*Operation Function:*
[source,c]
----
for_each (index in shape) {
in_t value1 = tensor_read<in_t>(input1, shape1, index)
in_t acc = !value1
tensor_write<in_t>(output, shape, index, acc)
}
----
*Supported Data Types:*
|===
|Profile|Mode|in_t
|Any|bool|bool
|===
==== NEGATE
Elementwise negation operation
*Arguments:*
|===
|Argument|Type|Name|Shape|Description
|Input|in_t*|input1|shape|Input tensor
|Output|in_t*|output|shape|Output tensor of same type, size as the input tensor
|===
*Quantization Parameters:*
|===
|Argument|Type|Name|Shape|Description
|Attribute|in_t|input1_zp|-|Input 1 zero point
|Attribute|in_t|output_zp|-|Output zero point
|===
*Operation Function:*
[source,c]
----
assert(in_t == aint8_t || input_zp == 0) // Zero point only for asymmetric int8
assert(in_t == aint8_t || output_zp == 0) // Zero point only for asymmetric int8
for_each (index in shape) {
in_t value1 = tensor_read<in_t>(input1, shape, index)
in_t acc = appl_sub<in_t>(0, value1 - input1_zp)
acc = apply_clip(acc, minimum<in_t>, maximum<in_t>)
tensor_write<in_t>(output + output_zp, shape, index, acc)
}
----
*Supported Data Types:*
|===
|Profile|Mode|in_t
|Any|signed 8|aint8
|Any|signed 16|int16
|Any|signed 32|int32
|MI, MT|float|float
|===
==== RECIPROCAL
Elementwise reciprocal operation. For integer operation, a TABLE should be used with the appropriate ranges.
*Arguments:*
|===
|Argument|Type|Name|Shape|Description
|Input|in_t*|input1|shape|Input tensor
|Output|in_t*|output|shape|Output tensor of same type, size as the input tensor
|===
*Supported Data Types:*
|===
|Profile|Mode|in_t
|MI, MT|float|float
|===
==== RSQRT
Elementwise reciprocal square root operation. For integer operation, a TABLE should be used with the appropriate ranges.
*Arguments:*
|===
|Argument|Type|Name|Shape|Description
|Input|in_t*|input1|shape|Input tensor
|Output|in_t*|output|shape|Output tensor of same type, size as the input tensor
|===
*Supported Data Types:*
|===
|Profile|Mode|in_t
|MI, MT|float|float
|===
|
