Merge branch 'master' of https://github.com/bwdeng20/pytorch_sparse

csrc/cpu/padding_cpu.cpp

+#include "padding_cpu.h"
+
+#include "utils.h"
+
+std::tuple<torch::Tensor, torch::Tensor, torch::Tensor, torch::Tensor,
+           std::vector<int64_t>, std::vector<int64_t>>
+padded_index_cpu(torch::Tensor rowptr, torch::Tensor col,
+                 torch::Tensor rowcount, torch::Tensor binptr) {
+  CHECK_CPU(rowptr);
+  CHECK_CPU(col);
+  CHECK_CPU(rowcount);
+  CHECK_CPU(binptr);
+  CHECK_INPUT(rowptr.numel() == rowcount.numel() + 1);
+
+  ptrdiff_t B = binptr.numel() - 1;
+  ptrdiff_t N = rowcount.numel();
+
+  auto rowptr_data = rowptr.data_ptr<int64_t>();
+  auto col_data = col.data_ptr<int64_t>();
+  auto rowcount_data = rowcount.data_ptr<int64_t>();
+  auto binptr_data = binptr.data_ptr<int64_t>();
+
+  auto bin = torch::empty(N, col.options());
+  auto bin_data = bin.data_ptr<int64_t>();
+  auto idx = torch::empty(N, col.options());
+  auto idx_data = idx.data_ptr<int64_t>();
+
+  std::vector<int64_t> node_sizes(B), edge_sizes(B), max_degs(B),
+      node_offsets(B + 1), edge_offsets(B + 1);
+
+  int64_t deg, bin_idx = -1;
+  for (ptrdiff_t n = 0; n < N; n++) {
+    deg = rowcount_data[n];
+
+    for (ptrdiff_t b = 1; b <= B; b++) {
+      if (deg < binptr_data[b]) {
+        bin_idx = b - 1;
+        break;
+      }
+    }
+
+    if (bin_idx == -1) {
+      bin_idx = B - 1;
+    }
+
+    bin_data[n] = bin_idx;
+    idx_data[n] = node_sizes[bin_idx];
+
+    node_sizes[bin_idx] += 1;
+    max_degs[bin_idx] = std::max(max_degs[bin_idx], deg);
+  }
+
+  for (ptrdiff_t b = 0; b < B; b++) {
+    edge_sizes[b] = node_sizes[b] * max_degs[b];
+    node_offsets[b + 1] = node_offsets[b] + node_sizes[b];
+    edge_offsets[b + 1] = edge_offsets[b] + edge_sizes[b];
+  }
+
+  auto node_perm = torch::empty(N, col.options());
+  auto node_perm_data = node_perm.data_ptr<int64_t>();
+
+  auto E = edge_offsets[B];
+  auto row_perm = torch::empty(E, col.options());
+  auto row_perm_data = row_perm.data_ptr<int64_t>();
+  auto col_perm = torch::empty(E, col.options());
+  auto col_perm_data = col_perm.data_ptr<int64_t>();
+  auto edge_mask = torch::empty(E, col.options().dtype(torch::kBool));
+  auto edge_mask_data = edge_mask.data_ptr<bool>();
+
+  int64_t row_start = rowptr_data[0], row_end, edge_offset, offset;
+  for (ptrdiff_t n = 0; n < N; n++) {
+    bin_idx = bin_data[n];
+    offset = idx_data[n];
+
+    node_perm_data[node_offsets[bin_idx] + offset] = n;
+
+    row_end = rowptr_data[n + 1];
+    edge_offset = edge_offsets[bin_idx] + offset * max_degs[bin_idx];
+    for (ptrdiff_t e = 0; e < row_end - row_start; e++) {
+      row_perm_data[edge_offset + e] = n;
+      col_perm_data[edge_offset + e] = col_data[row_start + e];
+      edge_mask_data[edge_offset + e] = false;
+    }
+
+    for (ptrdiff_t e = row_end - row_start; e < max_degs[bin_data[n]]; e++) {
+      row_perm_data[edge_offset + e] = -1;
+      col_perm_data[edge_offset + e] = -1;
+      edge_mask_data[edge_offset + e] = true;
+    }
+
+    row_start = row_end;
+  }
+
+  return std::make_tuple(node_perm, row_perm, col_perm, edge_mask, node_sizes,
+                         edge_sizes);
+}
+
+torch::Tensor padded_index_select_cpu(torch::Tensor src, torch::Tensor index,
+                                      torch::Tensor fill_value) {
+  CHECK_CPU(src);
+  CHECK_CPU(index);
+  CHECK_INPUT(src.dim() == 2);
+  CHECK_INPUT(index.dim() == 1);
+
+  auto mask = index == -1;
+
+  auto out = src.index_select(0, index.masked_fill(mask, 0));
+  out.masked_fill_(mask.view({-1, 1}).expand_as(out), fill_value);
+
+  return out;
+}
+
+torch::Tensor padded_index_scatter_cpu(torch::Tensor src, torch::Tensor index,
+                                       int64_t N) {
+  CHECK_CPU(src);
+  CHECK_CPU(index);
+  CHECK_INPUT(src.dim() == 2);
+  CHECK_INPUT(index.dim() == 1);
+  CHECK_INPUT(src.size(0) == index.size(0));
+
+  auto mask = index == -1;
+  index = index.masked_fill(mask, N);
+
+  auto out = torch::zeros({N + 1, src.size(-1)}, src.options());
+  out.scatter_add_(0, index.view({-1, 1}).expand_as(src), src);
+  out = out.narrow(0, 0, N);
+
+  return out;
+}

csrc/cpu/padding_cpu.h

+#pragma once
+
+#include <torch/extension.h>
+
+std::tuple<torch::Tensor, torch::Tensor, torch::Tensor, torch::Tensor,
+           std::vector<int64_t>, std::vector<int64_t>>
+padded_index_cpu(torch::Tensor rowptr, torch::Tensor col,
+                 torch::Tensor rowcount, torch::Tensor binptr);
+
+torch::Tensor padded_index_select_cpu(torch::Tensor src, torch::Tensor index,
+                                      torch::Tensor fill_value);
+
+torch::Tensor padded_index_scatter_cpu(torch::Tensor src, torch::Tensor index,
+                                       int64_t N);

csrc/cpu/rw_cpu.cpp

+#include "rw_cpu.h"
+
+#include "utils.h"
+
+torch::Tensor random_walk_cpu(torch::Tensor rowptr, torch::Tensor col,
+                              torch::Tensor start, int64_t walk_length) {
+  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 rand = torch::rand({start.size(0), walk_length},
+                          start.options().dtype(torch::kFloat));
+
+  auto L = walk_length + 1;
+  auto out = torch::full({start.size(0), L}, -1, 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 rand_data = rand.data_ptr<float>();
+  auto out_data = out.data_ptr<int64_t>();
+
+  for (auto n = 0; n < start.size(0); n++) {
+    auto cur = start_data[n];
+    out_data[n * L] = cur;
+
+    int64_t row_start, row_end;
+    for (auto l = 0; l < walk_length; l++) {
+      row_start = rowptr_data[cur];
+      row_end = rowptr_data[cur + 1];
+
+      cur = col_data[row_start + int64_t(rand_data[n * walk_length + l] *
+                                         (row_end - row_start))];
+      out_data[n * L + l + 1] = cur;
+    }
+  }
+
+  return out;
+}

csrc/cpu/rw_cpu.h

+#pragma once
+
+#include <torch/extension.h>
+
+torch::Tensor random_walk_cpu(torch::Tensor rowptr, torch::Tensor col,
+                              torch::Tensor start, int64_t walk_length);

csrc/cpu/saint_cpu.cpp

+#include "saint_cpu.h"
+
+#include "utils.h"
+
+std::tuple<torch::Tensor, torch::Tensor, torch::Tensor>
+subgraph_cpu(torch::Tensor idx, torch::Tensor rowptr, torch::Tensor row,
+             torch::Tensor col) {
+  CHECK_CPU(idx);
+  CHECK_CPU(rowptr);
+  CHECK_CPU(col);
+
+  CHECK_INPUT(idx.dim() == 1);
+  CHECK_INPUT(rowptr.dim() == 1);
+  CHECK_INPUT(col.dim() == 1);
+
+  auto assoc = torch::full({rowptr.size(0) - 1}, -1, idx.options());
+  assoc.index_copy_(0, idx, torch::arange(idx.size(0), idx.options()));
+
+  auto idx_data = idx.data_ptr<int64_t>();
+  auto rowptr_data = rowptr.data_ptr<int64_t>();
+  auto col_data = col.data_ptr<int64_t>();
+  auto assoc_data = assoc.data_ptr<int64_t>();
+
+  std::vector<int64_t> rows, cols, indices;
+
+  int64_t v, w, w_new, row_start, row_end;
+  for (int64_t v_new = 0; v_new < idx.size(0); v_new++) {
+    v = idx_data[v_new];
+    row_start = rowptr_data[v];
+    row_end = rowptr_data[v + 1];
+
+    for (int64_t j = row_start; j < row_end; j++) {
+      w = col_data[j];
+      w_new = assoc_data[w];
+      if (w_new > -1) {
+        rows.push_back(v_new);
+        cols.push_back(w_new);
+        indices.push_back(j);
+      }
+    }
+  }
+
+  int64_t length = rows.size();
+  row = torch::from_blob(rows.data(), {length}, row.options()).clone();
+  col = torch::from_blob(cols.data(), {length}, row.options()).clone();
+  idx = torch::from_blob(indices.data(), {length}, row.options()).clone();
+
+  return std::make_tuple(row, col, idx);
+}

csrc/cpu/saint_cpu.h

+#pragma once
+
+#include <torch/extension.h>
+
+std::tuple<torch::Tensor, torch::Tensor, torch::Tensor>
+subgraph_cpu(torch::Tensor idx, torch::Tensor rowptr, torch::Tensor row,
+             torch::Tensor col);

csrc/cuda/atomics.cuh

+#pragma once
+
+static inline __device__ void atomAdd(float *address, float val) {
+  atomicAdd(address, val);
+}
+
+static inline __device__ void atomAdd(double *address, double val) {
+#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ < 600 || CUDA_VERSION < 8000)
+  unsigned long long int *address_as_ull = (unsigned long long int *)address;
+  unsigned long long int old = *address_as_ull;
+  unsigned long long int assumed;
+
+  do {
+    assumed = old;
+    old = atomicCAS(address_as_ull, assumed,
+                    __double_as_longlong(val + __longlong_as_double(assumed)));
+  } while (assumed != old);
+#else
+  atomicAdd(address, val);
+#endif
+}

csrc/cuda/padding_cuda.cu

+#include "padding_cuda.h"
+
+#include <ATen/cuda/CUDAContext.h>
+
+#include "atomics.cuh"
+#include "utils.cuh"
+
+#define THREADS 1024
+#define FULL_MASK 0xffffffff
+#define BLOCKS(N) (N + THREADS - 1) / THREADS
+
+__global__ void bin_kernel(const int64_t *__restrict__ rowcount,
+                           const int64_t *__restrict__ binptr,
+                           int64_t *__restrict__ bin, int64_t *__restrict__ idx,
+                           int *__restrict__ node_size,
+                           int *__restrict__ max_deg, const size_t B,
+                           const size_t N) {
+
+  for (ptrdiff_t thread_idx = blockDim.x * blockIdx.x + threadIdx.x;
+       thread_idx < N; thread_idx += gridDim.x * blockDim.x) {
+
+    int bin_idx = -1, deg = rowcount[thread_idx];
+    for (ptrdiff_t b = 1; b <= B; b++) {
+      if (deg < __ldg(binptr + b)) {
+        bin_idx = b - 1;
+        break;
+      }
+    }
+
+    if (bin_idx == -1) {
+      bin_idx = B - 1;
+    }
+
+    int old = atomicAdd(node_size + bin_idx, 1);
+    atomicMax(max_deg + bin_idx, deg);
+
+    bin[thread_idx] = bin_idx;
+    idx[thread_idx] = old;
+  }
+}
+
+__global__ void info_kernel(const int *__restrict__ node_size,
+                            const int *__restrict__ max_deg,
+                            int *__restrict__ edge_size,
+                            int *__restrict__ node_offset,
+                            int *__restrict__ edge_offset, const size_t B) {
+
+  int thread_idx = blockDim.x * blockIdx.x + threadIdx.x;
+  int bin_idx = thread_idx / 32;
+  int lane_idx = thread_idx % 32;
+
+  if (bin_idx <= B) { // Computes `node_offset` and `edge_offset`.
+    int node_tmp = 0;
+    int edge_tmp = 0;
+
+    for (int i = lane_idx; i < bin_idx; i += 32) {
+      node_tmp += node_size[i];
+      edge_tmp += node_size[i] * max_deg[i];
+    }
+
+    for (int i = 32 / 2; i > 0; i /= 2) {
+      node_tmp += __shfl_down_sync(FULL_MASK, node_tmp, i);
+      edge_tmp += __shfl_down_sync(FULL_MASK, edge_tmp, i);
+    }
+
+    if (lane_idx == 0) {
+      node_offset[bin_idx] = node_tmp;
+      edge_offset[bin_idx] = edge_tmp;
+    }
+  } else if (bin_idx == B + 1) { // Computes `edge_size`.
+    for (int i = lane_idx; i < B; i += 32) {
+      edge_size[i] = node_size[i] * max_deg[i];
+    }
+  }
+}
+
+__global__ void node_perm_kernel(const int64_t *__restrict__ bin,
+                                 const int64_t *__restrict__ idx,
+                                 const int *__restrict__ node_offset,
+                                 int64_t *__restrict__ out, const size_t N) {
+
+  for (ptrdiff_t thread_idx = blockDim.x * blockIdx.x + threadIdx.x;
+       thread_idx < N; thread_idx += gridDim.x * blockDim.x) {
+    out[__ldg(node_offset + bin[thread_idx]) + idx[thread_idx]] = thread_idx;
+  }
+}
+
+template <int TB>
+__global__ void padded_index_kernel(
+    const int64_t *__restrict__ rowptr, const int64_t *__restrict__ col,
+    const int64_t *__restrict__ rowcount, const int64_t *__restrict__ bin,
+    const int64_t *__restrict__ idx, const int *__restrict__ max_deg,
+    const int *__restrict__ edge_offset, int64_t *__restrict__ row_perm,
+    int64_t *__restrict__ col_perm, bool *__restrict__ edge_mask,
+    const size_t B, const size_t N) {
+
+  for (ptrdiff_t thread_idx = blockDim.x * blockIdx.x + threadIdx.x;
+       thread_idx < TB * N; thread_idx += gridDim.x * blockDim.x) {
+
+    int row_idx = thread_idx / TB;
+    int lane_idx = thread_idx % TB;
+
+    int64_t bin_idx = bin[row_idx];
+    int len = __ldg(max_deg + bin_idx);
+    int off = __ldg(edge_offset + bin_idx) + len * idx[row_idx];
+
+    int64_t row_start = rowptr[row_idx], deg = rowcount[row_idx];
+    int64_t row_tmp, col_tmp;
+    for (int i = lane_idx; i < len; i += TB) {
+      row_tmp = -1, col_tmp = -1;
+      if (i < deg) {
+        row_tmp = row_idx;
+        col_tmp = col[row_start + i];
+      }
+      row_perm[off + i] = row_tmp;
+      col_perm[off + i] = col_tmp;
+      edge_mask[off + i] = row_tmp == -1;
+    }
+  }
+}
+
+std::tuple<torch::Tensor, torch::Tensor, torch::Tensor, torch::Tensor,
+           std::vector<int64_t>, std::vector<int64_t>>
+padded_index_cuda(torch::Tensor rowptr, torch::Tensor col,
+                  torch::Tensor rowcount, torch::Tensor binptr) {
+  CHECK_CUDA(rowptr);
+  CHECK_CUDA(col);
+  CHECK_CUDA(rowcount);
+  CHECK_CUDA(binptr);
+  CHECK_INPUT(rowptr.numel() == rowcount.numel() + 1);
+
+  cudaSetDevice(rowcount.get_device());
+  auto stream = at::cuda::getCurrentCUDAStream();
+  size_t mpc = at::cuda::getCurrentDeviceProperties()->multiProcessorCount;
+
+  size_t B = binptr.numel() - 1;
+  size_t N = rowcount.numel();
+
+  auto bin = torch::empty(N, col.options());
+  auto idx = torch::empty(N, col.options());
+
+  auto d_info = torch::zeros(5 * B + 2, col.options().dtype(torch::kInt));
+  auto d_node_size = d_info.narrow(0, 0, B);
+  auto d_edge_size = d_info.narrow(0, B, B);
+  auto d_max_deg = d_info.narrow(0, 2 * B, B);
+  auto d_node_offset = d_info.narrow(0, 3 * B, B + 1);
+  auto d_edge_offset = d_info.narrow(0, 4 * B + 1, B + 1);
+
+  bin_kernel<<<std::min(BLOCKS(N), mpc * 8), THREADS, 0, stream>>>(
+      rowcount.data_ptr<int64_t>(), binptr.data_ptr<int64_t>(),
+      bin.data_ptr<int64_t>(), idx.data_ptr<int64_t>(),
+      d_node_size.data_ptr<int>(), d_max_deg.data_ptr<int>(), B, N);
+
+  info_kernel<<<BLOCKS(32 * (B + 2)), THREADS, 0, stream>>>(
+      d_node_size.data_ptr<int>(), d_max_deg.data_ptr<int>(),
+      d_edge_size.data_ptr<int>(), d_node_offset.data_ptr<int>(),
+      d_edge_offset.data_ptr<int>(), B);
+
+  auto node_perm = torch::empty(N, col.options());
+
+  node_perm_kernel<<<std::min(BLOCKS(N), mpc * 8), THREADS, 0, stream>>>(
+      bin.data_ptr<int64_t>(), idx.data_ptr<int64_t>(),
+      d_node_offset.data_ptr<int>(), node_perm.data_ptr<int64_t>(), N);
+
+  auto h_info = torch::empty(
+      d_info.numel(), d_info.options().device(torch::kCPU).pinned_memory(true));
+  cudaMemcpy(h_info.data_ptr<int>(), d_info.data_ptr<int>(),
+             d_info.numel() * sizeof(int), cudaMemcpyDeviceToHost);
+
+  size_t E = h_info.data_ptr<int>()[5 * B + 1];
+  auto row_perm = torch::empty(E, col.options());
+  auto col_perm = torch::empty(E, col.options());
+  auto edge_mask = torch::empty(E, col.options().dtype(torch::kBool));
+
+  padded_index_kernel<8>
+      <<<std::min(BLOCKS(N * 8), mpc * 8), THREADS, 0, stream>>>(
+          rowptr.data_ptr<int64_t>(), col.data_ptr<int64_t>(),
+          rowcount.data_ptr<int64_t>(), bin.data_ptr<int64_t>(),
+          idx.data_ptr<int64_t>(), d_max_deg.data_ptr<int>(),
+          d_edge_offset.data_ptr<int>(), row_perm.data_ptr<int64_t>(),
+          col_perm.data_ptr<int64_t>(), edge_mask.data_ptr<bool>(), B, N);
+
+  h_info = h_info.to(torch::kLong);
+  auto h_info_data = h_info.data_ptr<int64_t>();
+  std::vector<int64_t> node_sizes(h_info_data, h_info_data + B);
+  std::vector<int64_t> edge_sizes(h_info_data + B, h_info_data + 2 * B);
+
+  return std::make_tuple(node_perm, row_perm, col_perm, edge_mask, node_sizes,
+                         edge_sizes);
+}
+
+template <typename scalar_t>
+__global__ void padded_index_select_kernel(const scalar_t *__restrict__ src,
+                                           const int64_t *__restrict__ index,
+                                           scalar_t *__restrict__ out,
+                                           const scalar_t fill_value,
+                                           const size_t E, const size_t F) {
+
+  for (ptrdiff_t thread_idx = blockDim.x * blockIdx.x + threadIdx.x;
+       thread_idx < E * F; thread_idx += gridDim.x * blockDim.x) {
+
+    int64_t row_idx = thread_idx / F;
+    int64_t lane_idx = thread_idx % F;
+    int64_t index_idx = __ldg(index + row_idx);
+
+    scalar_t tmp = fill_value;
+    if (index_idx != -1) {
+      tmp = src[index_idx * F + lane_idx];
+    }
+
+    out[thread_idx] = tmp;
+  }
+}
+
+torch::Tensor padded_index_select_cuda(torch::Tensor src, torch::Tensor index,
+                                       torch::Tensor fill_value) {
+  CHECK_CUDA(src);
+  CHECK_CUDA(index);
+  CHECK_INPUT(src.dim() == 2);
+  CHECK_INPUT(index.dim() == 1);
+
+  cudaSetDevice(src.get_device());
+  auto stream = at::cuda::getCurrentCUDAStream();
+  size_t mpc = at::cuda::getCurrentDeviceProperties()->multiProcessorCount;
+
+  size_t E = index.numel();
+  size_t F = src.size(-1);
+
+  auto out = torch::empty({(int)E, (int)F}, src.options());
+
+  AT_DISPATCH_ALL_TYPES(src.scalar_type(), "padded_index_select_kernel", [&] {
+    scalar_t *fill;
+    if (fill_value.is_cuda()) {
+      fill = (scalar_t *)malloc(sizeof(scalar_t));
+      cudaMemcpy(fill, fill_value.data_ptr<scalar_t>(), sizeof(scalar_t),
+                 cudaMemcpyDeviceToHost);
+    } else {
+      fill = fill_value.data_ptr<scalar_t>();
+    }
+
+    padded_index_select_kernel<scalar_t>
+        <<<std::min(BLOCKS(E * F), mpc * 8), THREADS, 0, stream>>>(
+            src.data_ptr<scalar_t>(), index.data_ptr<int64_t>(),
+            out.data_ptr<scalar_t>(), fill[0], E, F);
+  });
+
+  return out;
+}
+
+template <typename scalar_t>
+__global__ void padded_index_scatter_kernel(const scalar_t *__restrict__ src,
+                                            const int64_t *__restrict__ index,
+                                            scalar_t *__restrict__ out,
+                                            const size_t E, const size_t F) {
+
+  for (ptrdiff_t thread_idx = blockDim.x * blockIdx.x + threadIdx.x;
+       thread_idx < E * F; thread_idx += gridDim.x * blockDim.x) {
+
+    int64_t index_idx = __ldg(index + thread_idx / F);
+    if (index_idx != -1) {
+      atomAdd(out + index_idx * F + thread_idx % F, src[thread_idx]);
+    }
+  }
+}
+
+torch::Tensor padded_index_scatter_cuda(torch::Tensor src, torch::Tensor index,
+                                        int64_t N) {
+  CHECK_CUDA(src);
+  CHECK_CUDA(index);
+  CHECK_INPUT(src.dim() == 2);
+  CHECK_INPUT(index.dim() == 1);
+  CHECK_INPUT(src.size(0) == index.size(0));
+
+  cudaSetDevice(src.get_device());
+  auto stream = at::cuda::getCurrentCUDAStream();
+  size_t mpc = at::cuda::getCurrentDeviceProperties()->multiProcessorCount;
+
+  size_t E = index.numel();
+  size_t F = src.size(-1);
+
+  auto out = torch::zeros({N, (int)F}, src.options());
+
+  AT_DISPATCH_FLOATING_TYPES(
+      src.scalar_type(), "padded_index_scatter_kernel", [&] {
+        padded_index_scatter_kernel<scalar_t>
+            <<<std::min(BLOCKS(E * F), mpc * 8), THREADS, 0, stream>>>(
+                src.data_ptr<scalar_t>(), index.data_ptr<int64_t>(),
+                out.data_ptr<scalar_t>(), E, F);
+      });
+
+  return out;
+}

csrc/cuda/padding_cuda.h

+#pragma once
+
+#include <torch/extension.h>
+
+std::tuple<torch::Tensor, torch::Tensor, torch::Tensor, torch::Tensor,
+           std::vector<int64_t>, std::vector<int64_t>>
+padded_index_cuda(torch::Tensor rowptr, torch::Tensor col,
+                  torch::Tensor rowcount, torch::Tensor binptr);
+
+torch::Tensor padded_index_select_cuda(torch::Tensor src, torch::Tensor index,
+                                       torch::Tensor fill_value);
+
+torch::Tensor padded_index_scatter_cuda(torch::Tensor src, torch::Tensor index,
+                                        int64_t N);

csrc/cuda/rw_cuda.cu

+#include "rw_cuda.h"
+
+#include <ATen/cuda/CUDAContext.h>
+
+#include "utils.cuh"
+
+#define THREADS 1024
+#define BLOCKS(N) (N + THREADS - 1) / THREADS
+
+__global__ void uniform_random_walk_kernel(const int64_t *rowptr,
+                                           const int64_t *col,
+                                           const int64_t *start,
+                                           const float *rand, int64_t *out,
+                                           int64_t walk_length, int64_t numel) {
+  const int64_t thread_idx = blockIdx.x * blockDim.x + threadIdx.x;
+
+  if (thread_idx < numel) {
+    int64_t cur = start[thread_idx];
+    out[thread_idx] = cur;
+
+    int64_t row_start, row_end;
+    for (int64_t l = 0; l < walk_length; l++) {
+      row_start = rowptr[cur], row_end = rowptr[cur + 1];
+      cur = col[row_start +
+                int64_t(rand[l * numel + thread_idx] * (row_end - row_start))];
+      out[(l + 1) * numel + thread_idx] = cur;
+    }
+  }
+}
+
+torch::Tensor random_walk_cuda(torch::Tensor rowptr, torch::Tensor col,
+                               torch::Tensor start, int64_t walk_length) {
+  CHECK_CUDA(rowptr);
+  CHECK_CUDA(col);
+  CHECK_CUDA(start);
+  cudaSetDevice(rowptr.get_device());
+
+  CHECK_INPUT(rowptr.dim() == 1);
+  CHECK_INPUT(col.dim() == 1);
+  CHECK_INPUT(start.dim() == 1);
+
+  auto rand = torch::rand({walk_length, start.size(0)},
+                          start.options().dtype(torch::kFloat));
+  auto out = torch::full({walk_length + 1, start.size(0)}, -1, start.options());
+
+  auto stream = at::cuda::getCurrentCUDAStream();
+  uniform_random_walk_kernel<<<BLOCKS(start.numel()), THREADS, 0, stream>>>(
+      rowptr.data_ptr<int64_t>(), col.data_ptr<int64_t>(),
+      start.data_ptr<int64_t>(), rand.data_ptr<float>(),
+      out.data_ptr<int64_t>(), walk_length, start.numel());
+
+  return out.t().contiguous();
+}

csrc/cuda/rw_cuda.h

+#pragma once
+
+#include <torch/extension.h>
+
+torch::Tensor random_walk_cuda(torch::Tensor rowptr, torch::Tensor col,
+                               torch::Tensor start, int64_t walk_length);

csrc/padding.cpp

+#include <Python.h>
+#include <torch/script.h>
+
+#include "cpu/padding_cpu.h"
+
+#ifdef WITH_CUDA
+#include "cuda/padding_cuda.h"
+#endif
+
+#ifdef _WIN32
+PyMODINIT_FUNC PyInit__padding(void) { return NULL; }
+#endif
+
+std::tuple<torch::Tensor, torch::Tensor, torch::Tensor, torch::Tensor,
+           std::vector<int64_t>, std::vector<int64_t>>
+padded_index(torch::Tensor rowptr, torch::Tensor col, torch::Tensor rowcount,
+             torch::Tensor binptr) {
+  if (rowptr.device().is_cuda()) {
+#ifdef WITH_CUDA
+    return padded_index_cuda(rowptr, col, rowcount, binptr);
+#else
+    AT_ERROR("Not compiled with CUDA support");
+#endif
+  } else {
+    return padded_index_cpu(rowptr, col, rowcount, binptr);
+  }
+}
+
+using torch::autograd::AutogradContext;
+using torch::autograd::Variable;
+using torch::autograd::variable_list;
+
+class PaddedIndexSelect : public torch::autograd::Function<PaddedIndexSelect> {
+public:
+  static variable_list forward(AutogradContext *ctx, Variable src,
+                               Variable index, Variable fill_value) {
+    ctx->saved_data["N"] = src.size(0);
+
+    torch::Tensor out;
+    if (src.device().is_cuda()) {
+#ifdef WITH_CUDA
+      out = padded_index_select_cuda(src, index, fill_value);
+#else
+      AT_ERROR("Not compiled with CUDA support");
+#endif
+    } else {
+      out = padded_index_select_cpu(src, index, fill_value);
+    }
+    ctx->save_for_backward({index});
+    return {out};
+  }
+
+  static variable_list backward(AutogradContext *ctx, variable_list grad_outs) {
+    auto grad_out = grad_outs[0];
+    auto saved = ctx->get_saved_variables();
+    auto index = saved[0];
+    auto N = ctx->saved_data["N"].toInt();
+    torch::Tensor grad_in;
+    if (grad_out.device().is_cuda()) {
+#ifdef WITH_CUDA
+      grad_in = padded_index_scatter_cuda(grad_out, index, N);
+#else
+      AT_ERROR("Not compiled with CUDA support");
+#endif
+    } else {
+      grad_in = padded_index_scatter_cpu(grad_out, index, N);
+    }
+    return {grad_in, Variable(), Variable()};
+  }
+};
+
+torch::Tensor padded_index_select(torch::Tensor src, torch::Tensor index,
+                                  torch::Tensor fill_value) {
+  return PaddedIndexSelect::apply(src, index, fill_value)[0];
+}
+
+static auto registry =
+    torch::RegisterOperators()
+        .op("torch_sparse::padded_index", &padded_index)
+        .op("torch_sparse::padded_index_select", &padded_index_select);

csrc/rw.cpp

+#include <Python.h>
+#include <torch/script.h>
+
+#include "cpu/rw_cpu.h"
+
+#ifdef WITH_CUDA
+#include "cuda/rw_cuda.h"
+#endif
+
+#ifdef _WIN32
+PyMODINIT_FUNC PyInit__rw(void) { return NULL; }
+#endif
+
+torch::Tensor random_walk(torch::Tensor rowptr, torch::Tensor col,
+                          torch::Tensor start, int64_t walk_length) {
+  if (rowptr.device().is_cuda()) {
+#ifdef WITH_CUDA
+    return random_walk_cuda(rowptr, col, start, walk_length);
+#else
+    AT_ERROR("Not compiled with CUDA support");
+#endif
+  } else {
+    return random_walk_cpu(rowptr, col, start, walk_length);
+  }
+}
+
+static auto registry =
+    torch::RegisterOperators().op("torch_sparse::random_walk", &random_walk);

csrc/saint.cpp

+#include <Python.h>
+#include <torch/script.h>
+
+#include "cpu/saint_cpu.h"
+
+#ifdef _WIN32
+PyMODINIT_FUNC PyInit__saint(void) { return NULL; }
+#endif
+
+std::tuple<torch::Tensor, torch::Tensor, torch::Tensor>
+subgraph(torch::Tensor idx, torch::Tensor rowptr, torch::Tensor row,
+         torch::Tensor col) {
+  if (idx.device().is_cuda()) {
+#ifdef WITH_CUDA
+    AT_ERROR("No CUDA version supported");
+#else
+    AT_ERROR("Not compiled with CUDA support");
+#endif
+  } else {
+    return subgraph_cpu(idx, rowptr, row, col);
+  }
+}
+
+static auto registry =
+    torch::RegisterOperators().op("torch_sparse::saint_subgraph", &subgraph);

test/test_padding.py

+from itertools import product
+
+import pytest
+import torch
+from torch_sparse import SparseTensor, padded_index_select
+
+from .utils import grad_dtypes, devices, tensor
+
+
+@pytest.mark.parametrize('dtype,device', product(grad_dtypes, devices))
+def test_padded_index_select(dtype, device):
+    row = torch.tensor([0, 0, 0, 0, 1, 1, 1, 2, 2, 3])
+    col = torch.tensor([0, 1, 2, 3, 0, 2, 3, 1, 3, 2])
+    adj = SparseTensor(row=row, col=col).to(device)
+    binptr = torch.tensor([0, 3, 5], device=device)
+
+    data = adj.padded_index(binptr)
+    node_perm, row_perm, col_perm, mask, node_size, edge_size = data
+
+    assert node_perm.tolist() == [2, 3, 0, 1]
+    assert row_perm.tolist() == [2, 2, 3, -1, 0, 0, 0, 0, 1, 1, 1, -1]
+    assert col_perm.tolist() == [1, 3, 2, -1, 0, 1, 2, 3, 0, 2, 3, -1]
+    assert mask.long().tolist() == [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1]
+    assert node_size == [2, 2]
+    assert edge_size == [4, 8]
+
+    x = tensor([0, 1, 2, 3], dtype, device).view(-1, 1).requires_grad_()
+    x_j = padded_index_select(x, col_perm)
+
+    assert x_j.flatten().tolist() == [1, 3, 2, 0, 0, 1, 2, 3, 0, 2, 3, 0]
+
+    grad_out = tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], dtype, device)
+    x_j.backward(grad_out.view(-1, 1))
+
+    assert x.grad.flatten().tolist() == [12, 5, 17, 18]
+
+
+def test_padded_index_select_runtime():
+    return
+    from torch_geometric.datasets import Planetoid
+
+    device = torch.device('cuda')
+    start = torch.cuda.Event(enable_timing=True)
+    end = torch.cuda.Event(enable_timing=True)
+
+    dataset = Planetoid('/tmp/Planetoid', name='PubMed')
+    data = dataset[0]
+    row, col = data.edge_index.to(device)
+
+    adj = SparseTensor(row=row, col=col)
+    rowcount = adj.storage.rowcount().to(device)
+    rowptr = adj.storage.rowptr().to(device)
+    binptr = torch.tensor([0, 4, 11, 30, 50, 80, 120, 140, 2000]).to(device)
+
+    x = torch.randn(adj.size(0), 512).to(device)
+
+    data = torch.ops.torch_sparse.padded_index(rowptr, col, rowcount, binptr)
+    node_perm, row_perm, col_perm, mask, node_sizes, edge_sizes = data
+
+    out = torch.ops.torch_sparse.padded_index_select(x, col_perm,
+                                                     torch.tensor(0.))
+    outs = out.split(edge_sizes)
+    for out, size in zip(outs, node_sizes):
+        print(out.view(size, -1, x.size(-1)).shape)
+
+    for i in range(110):
+        if i == 10:
+            start.record()
+        torch.ops.torch_sparse.padded_index(rowptr, col, rowcount, binptr)
+    end.record()
+    torch.cuda.synchronize()
+    print('padded index', start.elapsed_time(end))
+
+    for i in range(110):
+        if i == 10:
+            start.record()
+        out = torch.ops.torch_sparse.padded_index_select(
+            x, col_perm, torch.tensor(0.))
+        out.split(edge_sizes)
+    end.record()
+    torch.cuda.synchronize()
+    print('padded index select', start.elapsed_time(end))
+
+    for i in range(110):
+        if i == 10:
+            start.record()
+        x.index_select(0, col)
+    end.record()
+    torch.cuda.synchronize()
+    print('index_select', start.elapsed_time(end))

test/test_saint.py

+import torch
+from torch_sparse.tensor import SparseTensor
+
+
+def test_saint_subgraph():
+    row = torch.tensor([0, 0, 1, 1, 2, 2, 2, 3, 3, 4])
+    col = torch.tensor([1, 2, 0, 2, 0, 1, 3, 2, 4, 3])
+    adj = SparseTensor(row=row, col=col)
+    node_idx = torch.tensor([0, 1, 2])
+
+    adj, edge_index = adj.saint_subgraph(node_idx)

torch_sparse/__init__.py

@@ -8,7 +8,8 @@ expected_torch_version = (1, 4)
 
 try:
     for library in [
-            '_version', '_convert', '_diag', '_spmm', '_spspmm', '_metis'
+            '_version', '_convert', '_diag', '_spmm', '_spspmm', '_metis',
+            '_rw', '_saint', '_padding'
     ]:
         torch.ops.load_library(importlib.machinery.PathFinder().find_spec(
             library, [osp.dirname(__file__)]).origin)
@@ -54,7 +55,10 @@ from .mul import mul, mul_, mul_nnz, mul_nnz_  # noqa
 from .reduce import sum, mean, min, max  # noqa
 from .matmul import matmul  # noqa
 from .cat import cat, cat_diag  # noqa
+from .rw import random_walk  # noqa
 from .metis import partition  # noqa
+from .saint import saint_subgraph  # noqa
+from .padding import padded_index, padded_index_select  # noqa
 
 from .convert import to_torch_sparse, from_torch_sparse  # noqa
 from .convert import to_scipy, from_scipy  # noqa
@@ -94,7 +98,11 @@ __all__ = [
     'matmul',
     'cat',
     'cat_diag',
+    'random_walk',
     'partition',
+    'saint_subgraph',
+    'padded_index',
+    'padded_index_select',
     'to_torch_sparse',
     'from_torch_sparse',
     'to_scipy',

torch_sparse/metis.py

@@ -34,5 +34,4 @@ def partition(src: SparseTensor, num_parts: int, recursive: bool = False
     return out, partptr, perm
 
 
-SparseTensor.partition = lambda self, num_parts, recursive=False: partition(
-    self, num_parts, recursive)
+SparseTensor.partition = partition

torch_sparse/padding.py

+from typing import Tuple, List
+
+import torch
+from torch_sparse.tensor import SparseTensor
+
+
+def padded_index(src: SparseTensor, binptr: torch.Tensor
+                 ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.
+                            Tensor, List[int], List[int]]:
+    return torch.ops.torch_sparse.padded_index(src.storage.rowptr(),
+                                               src.storage.col(),
+                                               src.storage.rowcount(), binptr)
+
+
+def padded_index_select(src: torch.Tensor, index: torch.Tensor,
+                        fill_value: float = 0.) -> torch.Tensor:
+    fill_value = torch.tensor(fill_value, dtype=src.dtype)
+    return torch.ops.torch_sparse.padded_index_select(src, index, fill_value)
+
+
+SparseTensor.padded_index = padded_index

torch_sparse/rw.py

+import torch
+from torch_sparse.tensor import SparseTensor
+
+
+def random_walk(src: SparseTensor, start: torch.Tensor,
+                walk_length: int) -> torch.Tensor:
+    rowptr, col, _ = src.csr()
+    return torch.ops.torch_sparse.random_walk(rowptr, col, start, walk_length)
+
+
+SparseTensor.random_walk = random_walk