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| -rw-r--r-- | chapters/ewise_binary.adoc | 40 |
1 files changed, 40 insertions, 0 deletions
diff --git a/chapters/ewise_binary.adoc b/chapters/ewise_binary.adoc index 6a5c575..18518bd 100644 --- a/chapters/ewise_binary.adoc +++ b/chapters/ewise_binary.adoc @@ -208,6 +208,46 @@ for_each(index in shape) { |Any|signed 32|int32_t |=== +==== DIV + +Elementwise divide of input1 by input2. +The result of the divide is truncated towards zero. +Only used for integer operation. +Floating point divide should use RECIPROCAL and MUL. + +*Arguments:* + +|=== +|Argument|Type|Name|Shape|Description + +|Input|in_t*|input1|shape1|Input tensor +|Input|in_t*|input2|shape2|Input tensor with the same rank as input1 +|Output|in_t*|output|shape|Output tensor 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); + assert(value2 != 0); + assert((int64_t)value1 / value2 <= maximum<in_t>); + in_t acc = value1 / value2; + tensor_write<in_t>(output, shape, index, acc); +} +---- + +*Supported Data Types:* +|=== +|Profile|Mode|in_t + +|Any|signed 32|int32_t +|=== + ==== LOGICAL_AND Elementwise logical AND of input1 and input2. |