apply_reflect_conj_tri_single Class — pytorch Architecture
Architecture documentation for the apply_reflect_conj_tri_single class in BatchLinearAlgebraKernel.cpp from the pytorch codebase.
Entity Profile
Source Code
aten/src/ATen/native/BatchLinearAlgebraKernel.cpp lines 73–99
template <typename scalar_t>
void apply_reflect_conj_tri_single(scalar_t* self, int64_t n, int64_t stride, bool upper) {
std::function<void(int64_t, int64_t)> loop = [](int64_t, int64_t){};
if (upper) {
loop = [&](int64_t start, int64_t end) {
for (const auto i : c10::irange(start, end)) {
for (int64_t j = i + 1; j < n; j++) {
self[i * stride + j] = conj_impl(self[j * stride + i]);
}
}
};
} else {
loop = [&](int64_t start, int64_t end) {
for (const auto i : c10::irange(start, end)) {
for (const auto j : c10::irange(i)) {
self[i * stride + j] = conj_impl(self[j * stride + i]);
}
}
};
}
// For small matrices OpenMP overhead is too large
if (n < 256) {
loop(0, n);
} else {
at::parallel_for(0, n, 0, loop);
}
}
Source
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