blob: b6d46cced01e8a13ba4a3bed8307db8f04649018 [file] [log] [blame]
//
// This confidential and proprietary software may be used only as
// authorised by a licensing agreement from ARM Limited
// (C) COPYRIGHT 2020-2024 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.
if (in_out_t == shape_t) {
ERROR_IF(rank(shape) != 0 || rank(shape1) != 0 || rank(shape2) != 0);
shape_t value1 = tensor_read<shape_t>(input1, [], []);
shape_t value2 = tensor_read<shape_t>(input2, [], []);
REQUIRE(value2 != 0);
shape_t result = value1 / value2;
tensor_write<shape_t>(output, [], [], result);
} else {
ERROR_IF(shape != broadcast_shape(shape1, shape2));
for_each(index in shape) {
dim_t index1 = apply_broadcast(shape, shape1, index);
dim_t index2 = apply_broadcast(shape, shape2, index);
in_out_t value1 = tensor_read<in_out_t>(input1, shape1, index1);
in_out_t value2 = tensor_read<in_out_t>(input2, shape2, index2);
REQUIRE(value2 != 0);
// This catches the case where we divide minimum<in_out_t> by -1
// which is not representable in two's complement
REQUIRE(static_cast<int64_t>(value1) / static_cast<int64_t>(value2) <= maximum_s<in_out_t>);
in_out_t result = apply_intdiv_s<in_out_t>(value1, value2);
tensor_write<in_out_t>(output, shape, index, result);
}
}