first weighted edges support commit

csrc/cpu/rw_cpu.cpp

@@ -107,6 +107,178 @@ void rejection_sampling(const int64_t *rowptr, const int64_t *col,
   });
 }
 
+
+void compute_cdf(const int64_t *rowptr, const float_t *edge_weight,
+		 float_t *edge_weight_cdf, int64_t numel) {
+  /* Convert edge weights to CDF as given in [1]
+  [1] https://github.com/louisabraham/fastnode2vec/blob/master/fastnode2vec/graph.py#L148
+  */
+  at::parallel_for(0, numel - 1, at::internal::GRAIN_SIZE, [&](int64_t begin, int64_t end) {
+    for(int64_t i = begin; i < end; i++) {
+      int64_t row_start = rowptr[i], row_end = rowptr[i + 1];
+
+      // Compute sum to normalize weights
+      float_t sum = 0.0;
+
+      for(int64_t j = row_start; j < row_end; j++) {
+        sum += edge_weight[j];
+      }
+
+      float_t acc = 0.0;
+
+      for(int64_t j = row_start; j < row_end; j++) {
+        acc += edge_weight[j] / sum;
+        edge_weight_cdf[j] = acc;
+      }
+    }
+  });
+}
+
+
+int64_t get_offset(const float_t *edge_weight, int64_t start, int64_t end) {
+  /*
+  The implementation given in [1] utilizes the `searchsorted` function in Numpy.
+  It is also available in PyTorch and its C++ API (via `at::searchsorted()`).
+  However, the implementation is adopted to the general case where the searched
+  values can be a multidimensional tensor. In our case, we have a 1D tensor of
+  edge weights (in form of a Cumulative Distribution Function) and a single
+  value, whose position we want to compute. To eliminate the overhead introduced
+  in the PyTorch implementation, one can examine the source code of
+  `searchsorted` [2] and find that for our case the whole function call can be
+  reduced to calling the `cus_lower_bound()` function. Unfortunately, we cannot
+  access it directly (the namespace is not exposed to the public API), but the
+  implementation is just a simple binary search. The code was copied here and
+  reduced to the bare minimum.
+  [1] https://github.com/louisabraham/fastnode2vec/blob/master/fastnode2vec/graph.py#L69
+  [2] https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Bucketization.cpp
+  */
+  float_t value = ((float_t)rand() / RAND_MAX); // [0, 1)
+  int64_t original_start = start;
+
+  while (start < end) {
+    const int64_t mid = start + ((end - start) >> 1);
+    const float_t mid_val = edge_weight[mid];
+    if (!(mid_val >= value)) {
+      start = mid + 1;
+    }
+    else {
+      end = mid;
+    }
+  }
+
+  return start - original_start;
+}
+
+// See: https://louisabraham.github.io/articles/node2vec-sampling.html
+// See also: https://github.com/louisabraham/fastnode2vec/blob/master/fastnode2vec/graph.py#L69
+void rejection_sampling_weighted(const int64_t *rowptr, const int64_t *col,
+                                 const float_t *edge_weight_cdf, 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 + get_offset(edge_weight_cdf, row_start, row_end);
+        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 {
+          if (p == 1. && q == 1.) {
+            e_cur = row_start + get_offset(edge_weight_cdf, row_start, row_end);
+            x = col[e_cur];
+          }
+          else {
+            while (true) {
+              e_cur = row_start + get_offset(edge_weight_cdf, row_start, row_end);
+              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_weighted_cpu(torch::Tensor rowptr, torch::Tensor col,
+                         torch::Tensor edge_weight, torch::Tensor start,
+                         int64_t walk_length, double p, double q) {
+  CHECK_CPU(rowptr);
+  CHECK_CPU(col);
+  CHECK_CPU(edge_weight);
+  CHECK_CPU(start);
+
+  CHECK_INPUT(rowptr.dim() == 1);
+  CHECK_INPUT(col.dim() == 1);
+  CHECK_INPUT(edge_weight.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 edge_weight_data = edge_weight.data_ptr<float_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>();
+
+  auto edge_weight_cdf = torch::empty({edge_weight.size(0)}, edge_weight.options());
+  auto edge_weight_cdf_data = edge_weight_cdf.data_ptr<float_t>();
+
+  compute_cdf(rowptr_data, edge_weight_data, edge_weight_cdf_data, rowptr.numel());
+
+  rejection_sampling_weighted(rowptr_data, col_data, edge_weight_cdf_data,
+                              start_data, n_out_data, e_out_data, start.numel(),
+                              walk_length, p, q);
+
+  return std::make_tuple(n_out, e_out);
+}
+
+
 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) {

csrc/cpu/rw_cpu.h

@@ -5,3 +5,8 @@
 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);
+
+std::tuple<torch::Tensor, torch::Tensor>
+random_walk_weighted_cpu(torch::Tensor rowptr, torch::Tensor col,
+                         torch::Tensor edge_weight, torch::Tensor start,
+                         int64_t walk_length, double p, double q);

csrc/cuda/rw_cuda.cu

@@ -38,6 +38,170 @@ __global__ void uniform_sampling_kernel(const int64_t *rowptr,
   }
 }
 
+
+__global__ void cdf_kernel(const int64_t *rowptr, const float_t *edge_weight,
+		           float_t *edge_weight_cdf, int64_t numel) {
+  const int64_t thread_idx = blockIdx.x * blockDim.x + threadIdx.x;
+
+  if (thread_idx < numel - 1) {
+    int64_t row_start = rowptr[thread_idx], row_end = rowptr[thread_idx + 1];
+
+    float_t sum = 0.0;
+
+    for(int64_t i = row_start; i < row_end; i++) {
+      sum += edge_weight[i];
+    }
+
+    float_t acc = 0.0;
+
+    for(int64_t i = row_start; i < row_end; i++) {
+      acc += edge_weight[i] / sum;
+      edge_weight_cdf[i] = acc;
+    }
+  }
+}
+
+__device__ void get_offset(const float_t *edge_weight, int64_t start, int64_t end,
+                           float_t value, int64_t *position_out) {
+  int64_t original_start = start;
+
+  while (start < end) {
+    const int64_t mid = start + ((end - start) >> 1);
+    const float_t mid_val = edge_weight[mid];
+    if (!(mid_val >= value)) {
+      start = mid + 1;
+    }
+    else {
+      end = mid;
+    }
+  }
+
+  *position_out = start - original_start;
+}
+
+__global__ void
+rejection_sampling_weighted_kernel(unsigned int seed, const int64_t *rowptr,
+                                   const int64_t *col, const float_t *edge_weight_cdf,
+                                   const int64_t *start, int64_t *n_out,
+                                   int64_t *e_out, const int64_t walk_length,
+                                   const int64_t numel, const double p,
+                                   const double q) {
+
+  curandState_t state;
+  curand_init(seed, 0, 0, &state);
+
+  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;
+
+  const int64_t thread_idx = blockIdx.x * blockDim.x + threadIdx.x;
+
+  if (thread_idx < numel) {
+    int64_t t = start[thread_idx], v, x, e_cur, row_start, row_end, offset;
+
+    n_out[thread_idx] = t;
+
+    row_start = rowptr[t], row_end = rowptr[t + 1];
+
+    if (row_end - row_start == 0) {
+      e_cur = -1;
+      v = t;
+    } else {
+      get_offset(edge_weight_cdf, row_start, row_end, curand_uniform(&state), &offset);
+      e_cur = row_start + offset;
+      v = col[e_cur];
+    }
+
+    n_out[numel + thread_idx] = v;
+    e_out[thread_idx] = e_cur;
+
+    for (int64_t 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 {
+        if (p == 1. && q == 1.) {
+          get_offset(edge_weight_cdf, row_start, row_end, curand_uniform(&state), &offset);
+          e_cur = row_start + offset;
+          x = col[e_cur];
+        }
+        else {
+          while (true) {
+            get_offset(edge_weight_cdf, row_start, row_end, curand_uniform(&state), &offset);
+            e_cur = row_start + offset;
+            x = col[e_cur];
+
+            double r = curand_uniform(&state); // (0, 1]
+
+            if (x == t && r < prob_0)
+              break;
+
+            bool is_neighbor = false;
+            row_start = rowptr[x], row_end = rowptr[x + 1];
+            for (int64_t i = row_start; i < row_end; i++) {
+              if (col[i] == t) {
+                is_neighbor = true;
+                break;
+              }
+            }
+
+            if (is_neighbor && r < prob_1)
+              break;
+            else if (r < prob_2)
+              break;
+          }
+        }
+      }
+
+      n_out[(l + 1) * numel + thread_idx] = x;
+      e_out[l * numel + thread_idx] = e_cur;
+      t = v;
+      v = x;
+    }
+  }
+}
+
+std::tuple<torch::Tensor, torch::Tensor>
+random_walk_weighted_cuda(torch::Tensor rowptr, torch::Tensor col,
+                          torch::Tensor edge_weight, torch::Tensor start,
+                          int64_t walk_length, double p, double q) {
+  CHECK_CUDA(rowptr);
+  CHECK_CUDA(col);
+  CHECK_CUDA(edge_weight);
+  CHECK_CUDA(start);
+  cudaSetDevice(rowptr.get_device());
+
+  CHECK_INPUT(rowptr.dim() == 1);
+  CHECK_INPUT(col.dim() == 1);
+  CHECK_INPUT(edge_weight.dim() == 1);
+  CHECK_INPUT(start.dim() == 1);
+
+  auto n_out = torch::empty({walk_length + 1, start.size(0)}, start.options());
+  auto e_out = torch::empty({walk_length, start.size(0)}, start.options());
+
+  auto stream = at::cuda::getCurrentCUDAStream();
+
+  auto edge_weight_cdf = torch::empty({edge_weight.size(0)}, edge_weight.options());
+
+  cdf_kernel<<<BLOCKS(rowptr.numel()), THREADS, 0, stream>>>(
+      rowptr.data_ptr<int64_t>(), edge_weight.data_ptr<float_t>(),
+      edge_weight_cdf.data_ptr<float_t>(), rowptr.numel());
+
+  rejection_sampling_weighted_kernel<<<BLOCKS(start.numel()), THREADS, 0, stream>>>(
+      time(NULL), rowptr.data_ptr<int64_t>(), col.data_ptr<int64_t>(),
+      edge_weight_cdf.data_ptr<float_t>(), start.data_ptr<int64_t>(),
+      n_out.data_ptr<int64_t>(), e_out.data_ptr<int64_t>(),
+      walk_length, start.numel(), p, q);
+
+  return std::make_tuple(n_out.t().contiguous(), e_out.t().contiguous());
+}
+
 __global__ void
 rejection_sampling_kernel(unsigned int seed, const int64_t *rowptr,
                           const int64_t *col, const int64_t *start,

csrc/cuda/rw_cuda.h

@@ -5,3 +5,8 @@
 std::tuple<torch::Tensor, torch::Tensor>
 random_walk_cuda(torch::Tensor rowptr, torch::Tensor col, torch::Tensor start,
                  int64_t walk_length, double p, double q);
+
+std::tuple<torch::Tensor, torch::Tensor>
+random_walk_weighted_cuda(torch::Tensor rowptr, torch::Tensor col,
+                          torch::Tensor edge_weight, torch::Tensor start,
+                          int64_t walk_length, double p, double q);

csrc/rw.cpp

@@ -33,5 +33,23 @@ random_walk(torch::Tensor rowptr, torch::Tensor col, torch::Tensor start,
   }
 }
 
+CLUSTER_API std::tuple<torch::Tensor, torch::Tensor>
+random_walk_weighted(torch::Tensor rowptr, torch::Tensor col,
+                     torch::Tensor edge_weight, torch::Tensor start,
+                     int64_t walk_length, double p, double q) {
+  if (rowptr.device().is_cuda()) {
+#ifdef WITH_CUDA
+    return random_walk_weighted_cuda(rowptr, col, edge_weight, start, walk_length, p, q);
+#else
+    AT_ERROR("Not compiled with CUDA support");
+#endif
+  } else {
+    return random_walk_weighted_cpu(rowptr, col, edge_weight, start, walk_length, p, q);
+  }
+}
+
 static auto registry =
-    torch::RegisterOperators().op("torch_cluster::random_walk", &random_walk);
+    torch::RegisterOperators().op("torch_cluster::random_walk", &random_walk)
+        .op("torch_cluster::random_walk_weighted", &random_walk_weighted);
+
+