//
// This confidential and proprietary software may be used only as
// authorised by a licensing agreement from ARM Limited
// (C) COPYRIGHT 2020-2021 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.

=== Type Conversion

==== CAST

Casts a tensor from one data type to another.

*Arguments:*

|===
|Argument|Type|Name|Shape|Description

|Input|in_t|input|shape|Input tensor
|Output|out_t|output|shape|Output tensor
|===

*Operation Function:*

[source,c]
....
for_each (index in shape) {
    in_t in = tensor_read<in_t>(input, shape, index);
    out_t out;
    if (out_t==bool) {
        out = (in!=0) ? true : false;
    } else if (in_t==bool) {
        out = (in) ? 1 : 0;
    } else if (out_t==float) {
        out = round_to_nearest_float(in);
    } else if (in_t==float) {
        out = apply_clip(round_to_nearest_int(in), minimum<out_t>, maximum<out_t>);
    } else if (sizeof(out_t)>=sizeof(in_t)) {
        out = sign_extend(in);
    } else {
        out = truncate(in);
    }
    tensor_write<out_t>(output, shape, index, out)
}
....

*Supported Data Types:*

|===
|Profile|Mode|in_t|out_t

|Any|bool to signed 8|bool|int8
|Any|bool to signed 16|bool|int16
|Any|bool to signed 32|bool|int32
|Any|signed 8 to bool|int8|bool
|Any|signed 8 to signed 16|int8|int16
|Any|signed 8 to signed 32|int8|int32
|Any|signed 8 to float|int8|float
|Any|signed 16 to bool|int16|bool
|Any|signed 16 to signed 8|int16|int8
|Any|signed 16 to signed 32|int16|int32
|Any|signed 16 to float|int16|float
|Any|signed 32 to bool|int32|bool
|Any|signed 32 to signed 8|int32|int8
|Any|signed 32 to signed 16|int32|int16
|Any|signed 32 to float|int32|float
|Any|float to signed 8|float|int8
|Any|float to signed 16|float|int16
|Any|float to signed 32|float|int32
|===

==== RESCALE

Rescale quantized values into a new domain. This function scales by factor: multiplier * 2^-shift^.

*Arguments:*

|===
|Argument|Type|Name|Shape|Description

|Input|in_t*|input|shape|Input tensor from 1 to 4 dims
|Output|out_t*|output|shape|Output tensor with the same shape as input
|===

*Quantization Parameters:*

|===
|Argument|Type|Name|Shape|Description

|Attribute|in_t|input_zp|-|Input tensor zero point
|Attribute|out_t|output_zp|-|Output tensor zero point
|Input (MT profile) Attribute (BI/MI profiles)|mul_t|multiplier[NC]|-|Scaling multiplier array
|Input (MT profile) Attribute (BI/MI profiles)|uint6_t|shift[NC] |-|Scaling shift array
|Input (MT profile) Attribute (BI/MI profiles)|bool|scale32|-|if (scale32) mul_t=int32_t else mul_t=int16_t
|Attribute|bool|double_round|-|Select double round mode
|Attribute|bool|per_channel|-|if (per_channel) NC=shape[dims-1] else NC=1
|===

*Operation Function:*

[source,c]
....
for_each (index in shape) {
    assert(in_t == int8  || in_t == uint8  || input_zp == 0);
    assert(out_t == int8 || out_t == uint8 || output_zp == 0);
    assert((scale32 && in_t!=int48_t) || (!scale32 && !double_round));
    int48_t value = tensor_read<in_t>(input, shape, index, input_zp);
    int c = (per_channel) ? index[dims-1] : 0;
    int32_t result = (scale32) ?
      apply_scale_32(value, multiplier[c], shift[c], double_round) :
      apply_scale_16(value, multiplier[c], shift[c]);
    result = apply_clip(result + output_zp, minimum<out_t>, maximum<out_t>)
    tensor_write<out_t>(output, shape, index, result)
}
....

*Supported Data Types:*

|===
|Profile|Mode|in_t|out_t

|Any|signed 8 to signed 8|int8|int8
|Any|signed 8 to signed 16|int8|int16
|Any|signed 8 to signed 32|int8|int32
|Any|signed 16 to signed 8|int16|int8
|Any|signed 16 to signed 16|int16|int16
|Any|signed 16 to signed 32|int16|int32
|Any|signed 32 to signed 8|int32|int8
|Any|signed 32 to signed 16|int32|int16
|Any|signed 32 to signed 32|int32|int32
|Any|signed 48 to signed 8|int48|int8
|Any|signed 48 to signed 16|int48|int16
|Any|signed 48 to signed 32|int48|int32
|Any|unsigned 8 to signed 8|uint8|int8
|Any|signed 8 to unsigned 8|int8|uint8
|===
