[Sparse] Add sparse matrix slicing operator implementation

dgl_sparse/include/sparse/sparse_matrix.h

@@ -137,6 +137,51 @@ class SparseMatrix : public torch::CustomClassHolder {
   static c10::intrusive_ptr<SparseMatrix> FromDiag(
       torch::Tensor value, const std::vector<int64_t>& shape);
 
+  /**
+   * @brief Create a SparseMatrix by selecting rows or columns based on provided
+   * indices.
+   *
+   * This function allows you to create a new SparseMatrix by selecting specific
+   * rows or columns from the original SparseMatrix based on the provided
+   * indices. The selection can be performed either row-wise or column-wise,
+   * determined by the 'dim' parameter.
+   *
+   * @param dim Select rows (dim=0) or columns (dim=1).
+   * @param ids A tensor containing the indices of the selected rows or columns.
+   *
+   * @return A new SparseMatrix containing the selected rows or columns.
+   *
+   * @note The 'dim' parameter should be either 0 (for row-wise selection) or 1
+   * (for column-wise selection).
+   * @note The 'ids' tensor should contain valid indices within the range of the
+   * original SparseMatrix's dimensions.
+   */
+  c10::intrusive_ptr<SparseMatrix> IndexSelect(int64_t dim, torch::Tensor ids);
+
+  /**
+   * @brief Create a SparseMatrix by selecting a range of rows or columns based
+   * on provided indices.
+   *
+   * This function allows you to create a new SparseMatrix by selecting a range
+   * of specific rows or columns from the original SparseMatrix based on the
+   * provided indices. The selection can be performed either row-wise or
+   * column-wise, determined by the 'dim' parameter.
+   *
+   * @param dim Select rows (dim=0) or columns (dim=1).
+   * @param start The starting index (inclusive) of the range.
+   * @param end The ending index (exclusive) of the range.
+   *
+   * @return A new SparseMatrix containing the selected range of rows or
+   * columns.
+   *
+   * @note The 'dim' parameter should be either 0 (for row-wise selection) or 1
+   * (for column-wise selection).
+   * @note The 'start' and 'end' indices should be valid indices within
+   * the valid range of the original SparseMatrix's dimensions.
+   */
+  c10::intrusive_ptr<SparseMatrix> RangeSelect(
+      int64_t dim, int64_t start, int64_t end);
+
   /**
    * @brief Create a SparseMatrix from a SparseMatrix using new values.
    * @param mat An existing sparse matrix

dgl_sparse/src/python_binding.cc

@@ -33,7 +33,9 @@ TORCH_LIBRARY(dgl_sparse, m) {
       .def("transpose", &SparseMatrix::Transpose)
       .def("coalesce", &SparseMatrix::Coalesce)
       .def("has_duplicate", &SparseMatrix::HasDuplicate)
-      .def("is_diag", &SparseMatrix::HasDiag);
+      .def("is_diag", &SparseMatrix::HasDiag)
+      .def("index_select", &SparseMatrix::IndexSelect)
+      .def("range_select", &SparseMatrix::RangeSelect);
   m.def("from_coo", &SparseMatrix::FromCOO)
       .def("from_csr", &SparseMatrix::FromCSR)
       .def("from_csc", &SparseMatrix::FromCSC)

dgl_sparse/src/sparse_matrix.cc

@@ -12,6 +12,8 @@
 #include <sparse/sparse_matrix.h>
 #include <torch/script.h>
 
+#include "./utils.h"
+
 namespace dgl {
 namespace sparse {
 
@@ -122,6 +124,49 @@ c10::intrusive_ptr<SparseMatrix> SparseMatrix::FromDiag(
   return SparseMatrix::FromDiagPointer(diag, value, shape);
 }
 
+c10::intrusive_ptr<SparseMatrix> SparseMatrix::IndexSelect(
+    int64_t dim, torch::Tensor ids) {
+  auto id_array = TorchTensorToDGLArray(ids);
+  bool rowwise = dim == 0;
+  auto csr = rowwise ? this->CSRPtr() : this->CSCPtr();
+  auto slice_csr = dgl::aten::CSRSliceRows(CSRToOldDGLCSR(csr), id_array);
+  auto slice_value =
+      this->value().index_select(0, DGLArrayToTorchTensor(slice_csr.data));
+  // To prevent potential errors in future conversions to the COO format,
+  // where this array might be used as an initialization array for
+  // constructing COO representations, it is necessary to clear this array.
+  slice_csr.data = dgl::aten::NullArray();
+  auto ret = CSRFromOldDGLCSR(slice_csr);
+  if (rowwise) {
+    return SparseMatrix::FromCSRPointer(
+        ret, slice_value, {ret->num_rows, ret->num_cols});
+  } else {
+    return SparseMatrix::FromCSCPointer(
+        ret, slice_value, {ret->num_cols, ret->num_rows});
+  }
+}
+
+c10::intrusive_ptr<SparseMatrix> SparseMatrix::RangeSelect(
+    int64_t dim, int64_t start, int64_t end) {
+  bool rowwise = dim == 0;
+  auto csr = rowwise ? this->CSRPtr() : this->CSCPtr();
+  auto slice_csr = dgl::aten::CSRSliceRows(CSRToOldDGLCSR(csr), start, end);
+  auto slice_value =
+      this->value().index_select(0, DGLArrayToTorchTensor(slice_csr.data));
+  // To prevent potential errors in future conversions to the COO format,
+  // where this array might be used as an initialization array for
+  // constructing COO representations, it is necessary to clear this array.
+  slice_csr.data = dgl::aten::NullArray();
+  auto ret = CSRFromOldDGLCSR(slice_csr);
+  if (rowwise) {
+    return SparseMatrix::FromCSRPointer(
+        ret, slice_value, {ret->num_rows, ret->num_cols});
+  } else {
+    return SparseMatrix::FromCSCPointer(
+        ret, slice_value, {ret->num_cols, ret->num_rows});
+  }
+}
+
 c10::intrusive_ptr<SparseMatrix> SparseMatrix::ValLike(
     const c10::intrusive_ptr<SparseMatrix>& mat, torch::Tensor value) {
   TORCH_CHECK(

python/dgl/sparse/sparse_matrix.py

@@ -487,7 +487,7 @@ def index_select(self, dim: int, index: torch.Tensor):
         dim : int
             The dim to select from matrix, should be 0 or 1. `dim = 0` for
             rowwise selection and `dim = 1` for columnwise selection.
-        index : tensor.Tensor
+        index : torch.Tensor
             The selection index indicates which IDs from the `dim` should
             be chosen from the matrix.
             Note that duplicated ids are allowed.
@@ -527,7 +527,7 @@ def index_select(self, dim: int, index: torch.Tensor):
         if dim not in (0, 1):
             raise ValueError("The selection dimension should be 0 or 1.")
         if isinstance(index, torch.Tensor):
-            raise NotImplementedError
+            return SparseMatrix(self.c_sparse_matrix.index_select(dim, index))
         raise TypeError(f"{type(index).__name__} is unsupported input type.")
 
     def range_select(self, dim: int, index: slice):
@@ -575,7 +575,15 @@ def range_select(self, dim: int, index: slice):
         if dim not in (0, 1):
             raise ValueError("The selection dimension should be 0 or 1.")
         if isinstance(index, slice):
-            raise NotImplementedError
+            if index.step not in (None, 1):
+                raise NotImplementedError(
+                    "Slice with step other than 1 are not supported yet."
+                )
+            start = 0 if index.start is None else index.start
+            end = index.stop
+            return SparseMatrix(
+                self.c_sparse_matrix.range_select(dim, start, end)
+            )
         raise TypeError(f"{type(index).__name__} is unsupported input type.")
 
 

tests/python/pytorch/sparse/test_sparse_matrix.py

@@ -18,6 +18,14 @@
     val_like,
 )
 
+from .utils import (
+    rand_coo,
+    rand_csc,
+    rand_csr,
+    rand_diag,
+    sparse_matrix_to_dense,
+)
+
 
 def _torch_sparse_csr_tensor(indptr, indices, val, torch_sparse_shape):
     with warnings.catch_warnings():
@@ -450,6 +458,52 @@ def test_has_duplicate():
     assert csc_A.has_duplicate()
 
 
+@pytest.mark.parametrize(
+    "create_func", [rand_diag, rand_csr, rand_csc, rand_coo]
+)
+@pytest.mark.parametrize("shape", [(5, 5), (6, 4)])
+@pytest.mark.parametrize("dense_dim", [None, 4])
+@pytest.mark.parametrize("select_dim", [0, 1])
+@pytest.mark.parametrize("index", [(0, 1, 3), (1, 2)])
+def test_index_select(create_func, shape, dense_dim, select_dim, index):
+    ctx = F.ctx()
+    A = create_func(shape, 20, ctx, dense_dim)
+    index = torch.tensor(index).to(ctx)
+    A_select = A.index_select(select_dim, index)
+
+    dense = sparse_matrix_to_dense(A)
+    dense_select = torch.index_select(dense, select_dim, index)
+
+    A_select_to_dense = sparse_matrix_to_dense(A_select)
+
+    assert A_select_to_dense.shape == dense_select.shape
+    assert torch.allclose(A_select_to_dense, dense_select)
+
+
+@pytest.mark.parametrize(
+    "create_func", [rand_diag, rand_csr, rand_csc, rand_coo]
+)
+@pytest.mark.parametrize("shape", [(5, 5), (6, 4)])
+@pytest.mark.parametrize("dense_dim", [None, 4])
+@pytest.mark.parametrize("select_dim", [0, 1])
+@pytest.mark.parametrize("rang", [slice(0, 2), slice(1, 3)])
+def test_range_select(create_func, shape, dense_dim, select_dim, rang):
+    ctx = F.ctx()
+    A = create_func(shape, 20, ctx, dense_dim)
+    A_select = A.range_select(select_dim, rang)
+
+    dense = sparse_matrix_to_dense(A)
+    if select_dim == 0:
+        dense_select = dense[rang, :]
+    else:
+        dense_select = dense[:, rang]
+
+    A_select_to_dense = sparse_matrix_to_dense(A_select)
+
+    assert A_select_to_dense.shape == dense_select.shape
+    assert torch.allclose(A_select_to_dense, dense_select)
+
+
 def test_print():
     ctx = F.ctx()