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//
// This confidential and proprietary software may be used only as
// authorised by a licensing agreement from ARM Limited
// (C) COPYRIGHT 2020-2021 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.
=== Data Layout
==== CONCAT
Concatenate two tensors along a given axis. No data conversion happens during a concat operation.
*Arguments:*
|===
|Argument|Type|Name|Shape|Description
|Input|in_t*|input1|shape1|Input tensor with rank from 1 to 4
|Input|in_t*|input2|shape2|Input tensor with rank matching input1
|Attribute|int|axis|-|Axis along which concatenation is to occur.
|Output|in_t*|output|shape|Output tensor of same type, size as the input tensor
|===
*Operation Function:*
[source,c]
----
for_each (index1 in shape) {
index2 = index1
index2[axis] = index1[axis] - shape1[axis]
in_t value = (index2[axis] < 0) ?
tensor_read<in_t>(input1, shape1, index1) :
tensor_read<in_t>(input2, shape2, index2) ;
tensor_write<in_t>(output, shape, index1, value);
}
----
*Supported Data Types:*
|===
|Profile|Mode|in_t
|Any|Boolean|bool
|Any|signed 8|int8
|Any|signed 16|int16
|Any|signed 32|int32
|MI, MT|float|float
|===
==== PAD
Zero-pads a tensor along borders of each dimension.
*Arguments:*
|===
|Argument|Type|Name|Shape|Description
|Input|in_t*|input1|shape1|Input tensor
|Attribute|int|padding|[rank(input1),2]|Amount of padding to be done
|Output|in_t*|output|shape|Output tensor of same type as the input tensor
|===
*Quantization Parameters:*
|===
|Argument|Type|Name|Shape|Description
|Attribute|in_t|input1_zp|-|Input tensor zero point
|===
*Operation Function:*
[source,c]
----
for_each (index in shape) {
index1 = index
for (i=0; i<rank(shape); i++) {
index1[i] = index1[i] - padding[i,0]
}
in_t value = tensor_read<in_t>(input1, shape1, index1, input1_zp, padding)
tensor_write<in_t>(output, shape, index, value + input1_zp);
}
----
*Supported Data Types:*
|===
|Profile|Mode|in_t
|Any|Boolean|bool
|Any|signed 8|int8
|Any|signed 16|int16
|Any|signed 32|int32
|MI, MT|float|float
|===
==== RESHAPE
Returns a tensor with the same type/values as the input, with a new shape specified by the shape argument. Reshape may operate on tensors of any rank. No data conversion happens during a reshape operation.
*Arguments:*
|===
|Argument|Type|Name|Shape|Description
|Input|in_t*|input1|shape1|Input tensor
|Attribute|int|new_shape|[rank(output)]|List of values, with each element giving the size of the result tensor for the given dimension. At most one dimension may be given as-1 to automatically calculate the dimension size.
|Output|in_t*|output|shape|Output tensor of same type, size as the input tensor
|===
*Operation Function:*
[source,c]
----
assert(tensor_size(shape1)==tensor_size(shape))
for (i=0; i<tensor_size(shape); i++) {
output[i] = input[i]
}
----
*Supported Data Types:*
|===
|Profile|Mode|in_t
|Any|Boolean|bool
|Any|signed 8|int8
|Any|signed 16|int16
|Any|signed 32|int32
|MI, MT|float|float
|===
==== REVERSE
Returns a tensor with the same type/values as the input, with the data reversed along the given axis. No data conversion happens during a reverse operation.
*Arguments:*
|===
|Argument|Type|Name|Shape|Description
|Input|in_t*|input|shape|Input tensor from 1 to 4 dims
|Attribute|int|axis|-|Axis to reverse
|Output|in_t*|output|shape|Output tensor. Same shape as input tensor.
|===
*Operation Function:*
[source,c]
----
assert(0<=axis && axis<rank(shape))
for_each (index in shape) {
tmp_index = index;
tmp_index[axis] = shape[axis]-1-index[axis];
in_t value = tensor_read<in_t>(input, shape, tmp_index);
tensor_write<in_t>(output, shape, index, value);
}
----
*Supported Data Types:*
|===
|Profile|Mode|in_t
|Any|Boolean|bool
|Any|signed 8|int8
|Any|signed 16|int16
|Any|signed 32|int32
|MI, MT|float|float
|===
==== SLICE
Extracts a slice of the input1 on the given axis, beginning at the start coordinates, and extending for size elements in each direction. No data conversion happens during a slice operation.
*Arguments:*
|===
|Argument|Type|Name|Shape|Description
|Input|in_t*|input1|shape1|Input tensor with rank from 1 to 4
|Attribute|int|start|[rank(input1)]|List of integer coordinates, of length equal to the rank of input1. Start coordinate for slicing.
|Attribute|int|size|[rank(input1)]|List of integer size values, of length equal to the rank of input1. Size of the input to be used.
|Output|in_t*|output|shape|Output tensor of same type as the input tensor
|===
*Operation Function:*
[source,c]
----
for_each (index in shape) {
tmp_index = index;
for (i=0; i<rank(shape); i++) {
tmp_index[i] = index[i] + start[i];
}
in_t value = tensor_read<in_t>(input, shape1, tmp_index);
tensor_write<in_t>(output, shape, index, value);
}
----
*Supported Data Types:*
|===
|Profile|Mode|in_t
|Any|Boolean|bool
|Any|signed 8|int8
|Any|signed 16|int16
|Any|signed 32|int32
|MI, MT|float|float
|===
==== TILE
Replicates input1 multiplies times along each dimension.
*Arguments:*
|===
|Argument|Type|Name|Shape|Description
|Input|in_t*|input1|shape1|Input tensor with rank from 1 to 4
|Attribute|int|multiplies|[rank(shape1)]|Number of times to replicate input1 in each dimension
|Output|in_t*|output|shape|Output tensor of same type, rank as the input tensor
|===
*Operation Function:*
[source,c]
----
for_each (index in shape) {
tmp_index = index;
for (i=0; i<rank(shape); i++) {
assert(shape1[i] * multiplies[i] == shape[i])
tmp_index[i] = index[i] % shape1[i]
}
in_t value = tensor_read<in_t>(input, shape1, tmp_index);
tensor_write<in_t>(output, shape, index, value);
}
----
*Supported Data Types:*
|===
|Profile|Mode|in_t
|Any|Boolean|bool
|Any|signed 8|int8
|Any|signed 16|int16
|Any|signed 32|int32
|MI, MT|float|float
|===
==== TRANSPOSE
Permutes the dimensions based on perm.
*Arguments:*
|===
|Argument|Type|Name|Shape|Description
|Input|in_t*|input1|shape1|Input tensor with rank from 1 to 4
|Attribute|int|perms|[rank(input1)]|List of integers of length equal to the rank of input1.
|Output|in_t*|output|shape|Output tensor of same type, rank as the input tensor
|===
*Operation Function:*
[source,c]
----
for_each (index in shape) {
tmp_index = index;
for (i=0; i<rank(shape); i++) {
assert(shape1[perm[i]] == shape[i])
tmp_index[perm[i]] = index[i]
}
in_t value = tensor_read<in_t>(input, shape1, tmp_index);
tensor_write<in_t>(output, shape, index, value);
}
----
*Supported Data Types:*
|===
|Profile|Mode|in_t
|Any|Boolean|bool
|Any|signed 8|int8
|Any|signed 16|int16
|Any|signed 32|int32
|MI, MT|float|float
|===
|
