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#include "rw_cpu.h"
#include <ATen/Parallel.h>
#include "utils.h"
void uniform_sampling(const int64_t *rowptr, const int64_t *col,
const int64_t *start, int64_t *n_out, int64_t *e_out,
const int64_t numel, const int64_t walk_length) {
auto rand = torch::rand({numel, walk_length});
auto rand_data = rand.data_ptr<float>();
int64_t grain_size = at::internal::GRAIN_SIZE / walk_length;
at::parallel_for(0, numel, grain_size, [&](int64_t begin, int64_t end) {
for (auto n = begin; n < end; n++) {
int64_t n_cur = start[n], e_cur, row_start, row_end, idx;
n_out[n * (walk_length + 1)] = n_cur;
for (auto l = 0; l < walk_length; l++) {
row_start = rowptr[n_cur], row_end = rowptr[n_cur + 1];
if (row_end - row_start == 0) {
e_cur = -1;
} else {
idx = int64_t(rand_data[n * walk_length + l] * (row_end - row_start));
e_cur = row_start + idx;
n_cur = col[e_cur];
}
n_out[n * (walk_length + 1) + (l + 1)] = n_cur;
e_out[n * walk_length + l] = e_cur;
}
}
});
}
bool inline is_neighbor(const int64_t *rowptr, const int64_t *col, int64_t v,
int64_t w) {
int64_t row_start = rowptr[v], row_end = rowptr[v + 1];
for (auto i = row_start; i < row_end; i++) {
if (col[i] == w)
return true;
}
return false;
}
// See: https://louisabraham.github.io/articles/node2vec-sampling.html
void rejection_sampling(const int64_t *rowptr, const int64_t *col,
int64_t *start, int64_t *n_out, int64_t *e_out,
const int64_t numel, const int64_t walk_length,
const double p, const double q) {
double max_prob = fmax(fmax(1. / p, 1.), 1. / q);
double prob_0 = 1. / p / max_prob;
double prob_1 = 1. / max_prob;
double prob_2 = 1. / q / max_prob;
int64_t grain_size = at::internal::GRAIN_SIZE / walk_length;
at::parallel_for(0, numel, grain_size, [&](int64_t begin, int64_t end) {
for (auto n = begin; n < end; n++) {
int64_t t = start[n], v, x, e_cur, row_start, row_end;
n_out[n * (walk_length + 1)] = t;
row_start = rowptr[t], row_end = rowptr[t + 1];
if (row_end - row_start == 0) {
e_cur = -1;
v = t;
} else {
e_cur = row_start + (rand() % (row_end - row_start));
v = col[e_cur];
}
n_out[n * (walk_length + 1) + 1] = v;
e_out[n * walk_length] = e_cur;
for (auto l = 1; l < walk_length; l++) {
row_start = rowptr[v], row_end = rowptr[v + 1];
if (row_end - row_start == 0) {
e_cur = -1;
x = v;
} else if (row_end - row_start == 1) {
e_cur = row_start;
x = col[e_cur];
} else {
while (true) {
e_cur = row_start + (rand() % (row_end - row_start));
x = col[e_cur];
auto r = ((double)rand() / (RAND_MAX)); // [0, 1)
if (x == t && r < prob_0)
break;
else if (is_neighbor(rowptr, col, x, t) && r < prob_1)
break;
else if (r < prob_2)
break;
}
}
n_out[n * (walk_length + 1) + (l + 1)] = x;
e_out[n * walk_length + l] = e_cur;
t = v;
v = x;
}
}
});
}
std::tuple<torch::Tensor, torch::Tensor>
random_walk_cpu(torch::Tensor rowptr, torch::Tensor col, torch::Tensor start,
int64_t walk_length, double p, double q) {
CHECK_CPU(rowptr);
CHECK_CPU(col);
CHECK_CPU(start);
CHECK_INPUT(rowptr.dim() == 1);
CHECK_INPUT(col.dim() == 1);
CHECK_INPUT(start.dim() == 1);
auto n_out = torch::empty({start.size(0), walk_length + 1}, start.options());
auto e_out = torch::empty({start.size(0), walk_length}, start.options());
auto rowptr_data = rowptr.data_ptr<int64_t>();
auto col_data = col.data_ptr<int64_t>();
auto start_data = start.data_ptr<int64_t>();
auto n_out_data = n_out.data_ptr<int64_t>();
auto e_out_data = e_out.data_ptr<int64_t>();
if (p == 1. && q == 1.) {
uniform_sampling(rowptr_data, col_data, start_data, n_out_data, e_out_data,
start.numel(), walk_length);
} else {
rejection_sampling(rowptr_data, col_data, start_data, n_out_data,
e_out_data, start.numel(), walk_length, p, q);
}
return std::make_tuple(n_out, e_out);
}
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