static std::array<int64_t, 2> as_array(at::IntList sizes) {
JIT_ASSERT(sizes.size() == 2);
std::array<int64_t, 2> arr;
arr[0] = sizes[0];
arr[1] = sizes[1];
return arr;
}
// First iterate over the 'from' tensor sizes. Ignore all leading and trailing
// dimensions that are simply one, since they can be trivially broadcasted.
// When iterating over the dimension sizes (with reduced 'from' tensor),
// starting at the trailing dimension, the dimension sizes must either be equal,
// or one of them does not exist.
//
// Note that this is NOT equivalent to numpy broadcasting semantics, and do
// not represent that generalized broadcasting that Pytorch implements in
// general. Rather, this is Caffe2-style broadcasting.
bool fusibleExpandTo(at::IntList from, at::IntList to) {
if (from.size() > to.size()) {
return false;
}
ssize_t from_dim_start = 0, from_dim_end = from.size() - 1;
while (from_dim_start < (ssize_t) from.size() && from[from_dim_start] == 1) {
from_dim_start++;
}
while (from_dim_end > from_dim_start && from[from_dim_end] == 1) {
from_dim_end--;
}
ssize_t f = from_dim_end;
ssize_t t = to.size() - 1;
for (; f >= from_dim_start && t >= 0; --f, --t) {
if (from[f] != to[t]) return false;
}
// In the case that the 'to' tensor has leading ones in the same place that
// the 'from' tensor does, f will be less than from_dim_start rather than
// strictly equal. E.x.: to := [5, 1, 768] and from := [1, 1, 768]
return f <= from_dim_start;
}
