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-rw-r--r--chapters/ewise_binary.adoc88
1 files changed, 52 insertions, 36 deletions
diff --git a/chapters/ewise_binary.adoc b/chapters/ewise_binary.adoc
index f44f7f5..4173aab 100644
--- a/chapters/ewise_binary.adoc
+++ b/chapters/ewise_binary.adoc
@@ -33,8 +33,8 @@ for_each(index in shape) {
index2 = apply_broadcast(shape, shape2, index);
in_t value1 = tensor_read<in_t>(input1, shape1, index1);
in_t value2 = tensor_read<in_t>(input2, shape2, index2);
- in_t acc = apply_add<in_t>(value1, value2);
- tensor_write<in_t>(output, shape, index, acc);
+ in_t result = apply_add<in_t>(value1, value2);
+ tensor_write<in_t>(output, shape, index, result);
----
*Supported Data Types:*
@@ -77,12 +77,12 @@ for_each(index in shape) {
(in_t == int16_t && 0 <= value2 && value2 <= 15) ||
(in_t == int8_t && 0 <= value2 && value2 <= 7));
- in_t acc = value1 >> value2;
+ in_t result = value1 >> value2;
if (round == true && value2 > 0 && (value1 >> (value2 - 1)) & 1 != 0) {
- acc = acc + 1;
+ result = result + 1;
}
- acc = apply_clip<in_t>(acc, minimum<in_t>, maximum<in_t>)
- tensor_write<in_t>(output, shape, index, acc)
+ result = apply_clip<in_t>(result, minimum<in_t>, maximum<in_t>);
+ tensor_write<in_t>(output, shape, index, result);
}
----
@@ -120,8 +120,8 @@ for_each(index in shape) {
index2 = apply_broadcast(shape, shape2, index);
in_t value1 = tensor_read<in_t>(input1, shape1, index1);
in_t value2 = tensor_read<in_t>(input2, shape2, index2);
- in_t acc = value1 & value2;
- tensor_write<in_t>(output, shape, index, acc);
+ in_t result = value1 & value2;
+ tensor_write<in_t>(output, shape, index, result);
}
----
@@ -159,8 +159,8 @@ for_each(index in shape) {
index2 = apply_broadcast(shape, shape2, index);
in_t value1 = tensor_read<in_t>(input1, shape1, index1);
in_t value2 = tensor_read<in_t>(input2, shape2, index2);
- in_t acc = value1 | value2;
- tensor_write<in_t>(output, shape, index, acc);
+ in_t result = value1 | value2;
+ tensor_write<in_t>(output, shape, index, result);
}
----
@@ -198,8 +198,8 @@ for_each(index in shape) {
index2 = apply_broadcast(shape, shape2, index);
in_t value1 = tensor_read<in_t>(input1, shape1, index1);
in_t value2 = tensor_read<in_t>(input2, shape2, index2);
- in_t acc = value1 ^ value2;
- tensor_write<in_t>(output, shape, index, acc);
+ in_t result = value1 ^ value2;
+ tensor_write<in_t>(output, shape, index, result);
}
----
@@ -244,8 +244,8 @@ for_each(index in shape) {
// This catches the case where we divide minimum<in_t> by -1
// which is not representable in two's complement
REQUIRE((int64_t)value1 / value2 <= maximum<in_t>);
- in_t acc = value1 / value2;
- tensor_write<in_t>(output, shape, index, acc);
+ in_t result = value1 / value2;
+ tensor_write<in_t>(output, shape, index, result);
}
----
@@ -280,8 +280,8 @@ for_each(index in shape) {
index2 = apply_broadcast(shape, shape2, index);
in_t value1 = tensor_read<in_t>(input1, shape1, index1);
in_t value2 = tensor_read<in_t>(input2, shape2, index2);
- in_t acc = value1 && value2;
- tensor_write<in_t>(output, shape, index, acc);
+ in_t result = value1 && value2;
+ tensor_write<in_t>(output, shape, index, result);
}
----
@@ -318,8 +318,8 @@ for_each(index in shape) {
in_t value1 = tensor_read<in_t>(input1, shape1, index1);
in_t value2 = tensor_read<in_t>(input2, shape2, index2);
REQUIRE(0 <= value2 && value2 <= 31);
- in_t acc = value1 << value2;
- tensor_write<in_t>(output, shape, index, acc);
+ in_t result = value1 << value2;
+ tensor_write<in_t>(output, shape, index, result);
}
----
@@ -358,8 +358,8 @@ for_each(index in shape) {
in_t value1 = tensor_read<in_t>(input1, shape1, index1);
in_t value2 = tensor_read<in_t>(input2, shape2, index2);
REQUIRE(0 <= value2 && value2 <= 31);
- in_t acc = (unsigned in_t)value1 >> value2;
- tensor_write<in_t>(output, shape, index, acc);
+ in_t result = (in_t)((unsigned in_t)value1 >> value2);
+ tensor_write<in_t>(output, shape, index, result);
}
----
@@ -397,8 +397,8 @@ for_each(index in shape) {
index2 = apply_broadcast(shape, shape2, index);
in_t value1 = tensor_read<in_t>(input1, shape1, index1);
in_t value2 = tensor_read<in_t>(input2, shape2, index2);
- in_t acc = value1 || value2;
- tensor_write<in_t>(output, shape, index, acc);
+ in_t result = value1 || value2;
+ tensor_write<in_t>(output, shape, index, result);
}
----
@@ -434,8 +434,8 @@ for_each(index in shape) {
index2 = apply_broadcast(shape, shape2, index);
in_t value1 = tensor_read<in_t>(input1, shape1, index1);
in_t value2 = tensor_read<in_t>(input2, shape2, index2);
- in_t acc = value1 != value2;
- tensor_write<in_t>(output, shape, index, acc);
+ in_t result = value1 != value2;
+ tensor_write<in_t>(output, shape, index, result);
}
----
@@ -471,8 +471,8 @@ for_each(index in shape) {
index2 = apply_broadcast(shape, shape2, index);
in_t value1 = tensor_read<in_t>(input1, shape1, index1);
in_t value2 = tensor_read<in_t>(input2, shape2, index2);
- in_t acc = apply_max(value1, value2);
- tensor_write<in_t>(output, shape, index, acc);
+ in_t result = apply_max(value1, value2);
+ tensor_write<in_t>(output, shape, index, result);
}
----
@@ -509,8 +509,8 @@ for_each(index in shape) {
index2 = apply_broadcast(shape, shape2, index);
in_t value1 = tensor_read<in_t>(input1, shape1, index1);
in_t value2 = tensor_read<in_t>(input2, shape2, index2);
- in_t acc = apply_min(value1, value2);
- tensor_write<in_t>(output, shape, index, acc);
+ in_t result = apply_min(value1, value2);
+ tensor_write<in_t>(output, shape, index, result);
}
----
@@ -548,12 +548,13 @@ for_each(index in shape) {
index2 = apply_broadcast(shape, shape2, index);
in_t value1 = tensor_read<in_t>(input1, shape1, index1);
in_t value2 = tensor_read<in_t>(input2, shape2, index2);
+ out_t result;
if (in_t == int32_t && shift > 0) {
- out_t acc = apply_scale_32(value1, value2, shift);
+ result = apply_scale_32(value1, value2, shift);
} else {
- out_t acc = value1 * value2; // low 32-bits of result for int32_t
+ result = value1 * value2; // low 32-bits of result for int32_t
}
- tensor_write<out_t>(output, shape, index, acc);
+ tensor_write<out_t>(output, shape, index, result);
}
----
@@ -582,6 +583,20 @@ Axis of size 1 will be broadcast, as necessary. Rank of input tensors must match
|Output|in_t*|output|shape|Output tensor of same type as the input tensors, with broadcast shape if necessary
|===
+*Operation Function:*
+
+[source,c++]
+----
+for_each(index in shape) {
+ index1 = apply_broadcast(shape, shape1, index);
+ index2 = apply_broadcast(shape, shape2, index);
+ in_t value1 = tensor_read<in_t>(input1, shape1, index1);
+ in_t value2 = tensor_read<in_t>(input2, shape2, index2);
+ in_t result = apply_pow<in_t>(value1, value2);
+ tensor_write<in_t>(output, shape, index, result);
+}
+----
+
*Supported Data Types:*
|===
@@ -614,8 +629,8 @@ for_each(index in shape) {
index2 = apply_broadcast(shape, shape2, index);
in_t value1 = tensor_read<in_t>(input1, shape1, index1);
in_t value2 = tensor_read<in_t>(input2, shape2, index2);
- in_t acc = apply_sub<in_t>(value1, value2);
- tensor_write<in_t>(output, shape, index, acc);
+ in_t result = apply_sub<in_t>(value1, value2);
+ tensor_write<in_t>(output, shape, index, result);
}
----
@@ -661,13 +676,14 @@ An int16_t to int16_t table lookup can be constructed in TOSA as follows:
REQUIRE(length(table) == TABLE_SIZE);
for_each(index in shape) {
in_t value = tensor_read<in_t>(input, shape, index);
+ out_t result;
if (in_t == int8_t) {
// value is a signed int, convert to a 0 based index
- out_t acc = table[value + 128];
+ result = table[value + 128];
} else {
- out_t acc = apply_lookup(table, value);
+ result = apply_lookup(table, value);
}
- tensor_write<out_t>(output, shape, index, acc);
+ tensor_write<out_t>(output, shape, index, result);
}
----
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-11 13:03:50 +0000