Sparse ops: Support elementwise ops between sparse and diagonal matrix (#4617)

* initial commit

* address comments

* lint check
Co-authored-by: Israt Nisa <nisisrat@amazon.com>

python/dgl/mock_sparse/elementwise_op_sp.py

 """dgl elementwise operators for sparse matrix module."""
+from typing import Union
+
 import torch
+
 from .sp_matrix import SparseMatrix
+from .diag_matrix import DiagMatrix
+
+__all__ = ["add", "sub", "mul", "div", "rdiv", "power", "rpower"]
 
-__all__ = ['add', 'sub', 'mul', 'div', 'rdiv', 'power', 'rpower']
 
-def add(A, B):
+def add(
+    A: Union[SparseMatrix, DiagMatrix], B: Union[SparseMatrix, DiagMatrix]
+) -> SparseMatrix:
     """Elementwise addition.
 
     Parameters
     ----------
-    A : SparseMatrix
-        Sparse matrix
-    B : SparseMatrix
-        Sparse matrix
+    A : SparseMatrix or DiagMatrix
+        Sparse matrix or diagonal matrix
+    B : SparseMatrix or DiagMatrix
+        Sparse matrix or diagonal matrix
 
     Returns
     -------
@@ -21,53 +28,70 @@ def add(A, B):
 
     Examples
     --------
-    Case 1: Add two matrices of same sparsity structure
+    Case 1: Add two sparse matrices of same sparsity structure
 
-    >>> rowA = torch.tensor([1, 0, 2, 7, 1])
-    >>> colA = torch.tensor([0, 49, 2, 1, 7])
-    >>> valA = torch.tensor([10, 20, 30, 40, 50])
-    >>> A = SparseMatrix(rowA, colA, valA, shape=(10, 50))
+    >>> rowA = torch.tensor([1, 0, 2])
+    >>> colA = torch.tensor([0, 3, 2])
+    >>> valA = torch.tensor([10, 20, 30])
+    >>> A = SparseMatrix(rowA, colA, valA, shape=(3, 4))
     >>> A + A
-    SparseMatrix(indices=tensor([[ 0,  1,  1,  2,  7],
-            [49,  0,  7,  2,  1]]),
-    values=tensor([ 40,  20, 100,  60,  80]),
-    shape=(10, 50), nnz=5)
+    SparseMatrix(indices=tensor([[0, 1, 2],
+            [3, 0, 2]]),
+    values=tensor([40, 20, 60]),
+    shape=(3, 4), nnz=3)
     >>> w = torch.arange(1, len(rowA)+1)
     >>> A + A(w)
-    SparseMatrix(indices=tensor([[ 0,  1,  1,  2,  7],
-            [49,  0,  7,  2,  1]]),
-    values=tensor([21, 12, 53, 34, 45]),
-    shape=(10, 50), nnz=5)
+    SparseMatrix(indices=tensor([[0, 1, 2],
+            [3, 0, 2]]),
+    values=tensor([21, 12, 33]),
+    shape=(3, 4), nnz=3)
 
-    Case 2: Add two matrices of different sparsity structure
+    Case 2: Add two sparse matrices of different sparsity structure
 
-    >>> rowB = torch.tensor([1, 9, 2, 7, 1, 1, 0])
-    >>> colB = torch.tensor([0, 1, 2, 1, 7, 11, 15])
-    >>> valB = torch.tensor([1, 2, 3, 4, 5, 6])
-    >>> B = SparseMatrix(rowB, colB, valB, shape=(10, 50))
+    >>> rowB = torch.tensor([1, 2, 0, 2, 1])
+    >>> colB = torch.tensor([0, 2, 1, 3, 3])
+    >>> valB = torch.tensor([1, 2, 3, 4, 5])
+    >>> B = SparseMatrix(rowB, colB, valB, shape=(3 ,4))
     >>> A + B
-    SparseMatrix(indices=tensor([[ 0, 1, 1, 1, 2, 7, 9],
-            [49, 0, 7, 11, 2, 1, 1]]),
-    values=tensor([20, 11, 55,  6, 33, 44,  2]),
-    shape=(10, 50), nnz=7)
+    SparseMatrix(indices=tensor([[0, 0, 1, 1, 2, 2],
+            [1, 3, 0, 3, 2, 3]]),
+    values=tensor([ 3, 20, 11,  5, 32,  4]),
+    shape=(3, 4), nnz=6)
+
+    Case 3: Add sparse matrix and diagonal matrix
+
+    >>> D = diag(torch.arange(2, 5), shape=A.shape)
+    >>> A + D
+    SparseMatrix(indices=tensor([[0, 0, 1, 1, 2],
+            [0, 3, 0, 1, 2]]),
+    values=tensor([ 2, 20, 10,  3, 34]),
+    shape=(3, 4), nnz=5)
     """
+    B = B.as_sparse() if isinstance(B, DiagMatrix) else B
     if isinstance(A, SparseMatrix) and isinstance(B, SparseMatrix):
-        assert A.shape == B.shape, 'The shape of sparse matrix A {} and' \
-        ' B {} are expected to match'.format(A.shape, B.shape)
+        assert A.shape == B.shape, (
+            "The shape of sparse matrix A {} and"
+            " B {} are expected to match".format(A.shape, B.shape)
+        )
         C = A.adj + B.adj
         return SparseMatrix(C.indices()[0], C.indices()[1], C.values(), C.shape)
-    raise RuntimeError('Elementwise addition between {} and {} is not ' \
-                       'supported.'.format(type(A), type(B)))
+    raise RuntimeError(
+        "Elementwise addition between {} and {} is not "
+        "supported.".format(type(A), type(B))
+    )
+
 
-def sub(A, B):
+def sub(
+    A: Union[SparseMatrix, DiagMatrix], B: Union[SparseMatrix, DiagMatrix]
+) -> SparseMatrix:
     """Elementwise subtraction.
 
     Parameters
     ----------
-    A : SparseMatrix
-        Sparse matrix
-    B : SparseMatrix
-        Sparse matrix
+    A : SparseMatrix or DiagMatrix
+        Sparse matrix or diagonal matrix
+    B : SparseMatrix or DiagMatrix
+        Sparse matrix or diagonal matrix
 
     Returns
     -------
@@ -76,37 +100,57 @@ def sub(A, B):
 
     Examples
     --------
-    >>> rowA = torch.tensor([1, 0, 2, 7, 1])
-    >>> colA = torch.tensor([0, 49, 2, 1, 7])
-    >>> valA = torch.tensor([10, 20, 30, 40, 50])
-    >>> A = SparseMatrix(rowA, colA, valA, shape=(10, 50))
-    >>> rowB = torch.tensor([1, 9, 2, 7, 1, 1])
-    >>> colB = torch.tensor([0, 1, 2, 1, 7, 11])
-    >>> valB = torch.tensor([1, 2, 3, 4, 5, 6])
-    >>> B = SparseMatrix(rowB, colB, valB, shape=(10, 50))
+    Case 1: Subtract two sparse matrices
+
+    >>> rowA = torch.tensor([1, 0, 2])
+    >>> colA = torch.tensor([0, 3, 2])
+    >>> valA = torch.tensor([10, 20, 30])
+    >>> A = SparseMatrix(rowA, colA, valA, shape=(3, 4))
+    >>> rowB = torch.tensor([1, 2, 0, 2, 1])
+    >>> colB = torch.tensor([0, 2, 1, 3, 3])
+    >>> valB = torch.tensor([1, 2, 3, 4, 5])
+    >>> B = SparseMatrix(rowB, colB, valB, shape=(3 ,4))
     >>> A - B
-    SparseMatrix(indices=tensor([[ 0, 1, 1, 1, 2, 7, 9],
-            [49, 0, 7, 11, 2, 1, 1]]),
-    values=tensor([20,  9, 45, -6, 27, 36, -2]),
-    shape=(10, 50), nnz=7
+    SparseMatrix(indices=tensor([[0, 0, 1, 1, 2, 2],
+            [1, 3, 0, 3, 2, 3]]),
+    values=tensor([-3, 20,  9, -5, 28, -4]),
+    shape=(3, 4), nnz=6)
+
+    Case 2: Subtract sparse matrix and diagonal matrix
+
+    >>> D = diag(torch.arange(2, 5), shape=A.shape)
+    >>> A - D
+    SparseMatrix(indices=tensor([[0, 0, 1, 1, 2],
+            [0, 3, 0, 1, 2]]),
+    values=tensor([-2, 20, 10, -3, 26]),
+    shape=(3, 4), nnz=5)
     """
+    B = B.as_sparse() if isinstance(B, DiagMatrix) else B
     if isinstance(A, SparseMatrix) and isinstance(B, SparseMatrix):
-        assert A.shape == B.shape, 'The shape of sparse matrix A {} and' \
-        ' B {} are expected to match.'.format(A.shape, B.shape)
+        assert A.shape == B.shape, (
+            "The shape of sparse matrix A {} and"
+            " B {} are expected to match.".format(A.shape, B.shape)
+        )
         C = A.adj - B.adj
         return SparseMatrix(C.indices()[0], C.indices()[1], C.values(), C.shape)
-    raise RuntimeError('Elementwise subtraction between {} and {} is not ' \
-                       'supported.'.format(type(A), type(B)))
+    raise RuntimeError(
+        "Elementwise subtraction between {} and {} is not "
+        "supported.".format(type(A), type(B))
+    )
+
 
-def mul(A, B):
+def mul(
+    A: Union[SparseMatrix, DiagMatrix, float],
+    B: Union[SparseMatrix, DiagMatrix, float],
+) -> SparseMatrix:
     """Elementwise multiplication.
 
     Parameters
     ----------
-    A : SparseMatrix or scalar
-        Sparse matrix or scalar value
-    B : SparseMatrix or scalar
-        Sparse matrix or scalar value.
+    A : SparseMatrix or DiagMatrix or scalar
+        Sparse matrix or diagonal matrix or scalar value
+    B : SparseMatrix or DiagMatrix or scalar
+        Sparse matrix or diagonal matrix or scalar value
 
     Returns
     -------
@@ -117,51 +161,62 @@ def mul(A, B):
     --------
     Case 1: Elementwise multiplication between two sparse matrices
 
-    >>> rowA = torch.tensor([1, 0, 2, 7, 1])
-    >>> colA = torch.tensor([0, 49, 2, 1, 7])
-    >>> valA = torch.tensor([10, 20, 30, 40, 50])
-    >>> A = SparseMatrix(rowA, colA, valA, shape=(10, 50))
-    >>> rowB = torch.tensor([1, 9, 2, 7, 1, 1])
-    >>> colB = torch.tensor([0, 1, 2, 1, 7, 11])
-    >>> valB = torch.tensor([1, 2, 3, 4, 5, 6])
-    >>> B = SparseMatrix(rowB, colB, valB, shape=(10, 50))
+    >>> rowA = torch.tensor([1, 0, 2])
+    >>> colA = torch.tensor([0, 3, 2])
+    >>> valA = torch.tensor([10, 20, 30])
+    >>> A = SparseMatrix(rowA, colA, valA, shape=(3, 4))
+    >>> rowB = torch.tensor([1, 2, 0, 2, 1])
+    >>> colB = torch.tensor([0, 2, 1, 3, 3])
+    >>> valB = torch.tensor([1, 2, 3, 4, 5])
+    >>> B = SparseMatrix(rowB, colB, valB, shape=(3 ,4))
     >>> A * B
-    SparseMatrix(indices=tensor([[1, 1, 2, 7],
-            [0, 7, 2, 1]]),
-    values=tensor([ 10, 250,  90, 160]),
-    shape=(10, 50), nnz=4)
+    SparseMatrix(indices=tensor([[1, 2],
+            [0, 2]]),
+    values=tensor([10, 60]),
+    shape=(3, 4), nnz=2)
 
-    Case 2: Elementwise multiplication between sparse matrix and scalar
+    Case 2: Elementwise multiplication between sparse matrix and scalar value
 
     >>> v_scalar = 2.5
     >>> A * v_scalar
-    SparseMatrix(indices=tensor([[ 0,  1,  1,  2,  7],
-            [49,  0,  7,  2,  1]]),
-    values=tensor([ 50.,  25., 125.,  75., 100.]),
-    shape=(8, 50), nnz=5)
-    >>> v_scalar * A
-    SparseMatrix(indices=tensor([[ 0,  1,  1,  2,  7],
-            [49,  0,  7,  2,  1]]),
-    values=tensor([ 50.,  25., 125.,  75., 100.]),
-    shape=(8, 50), nnz=5)
+    SparseMatrix(indices=tensor([[0, 1, 2],
+            [3, 0, 2]]),
+    values=tensor([50., 25., 75.]),
+    shape=(3, 4), nnz=3)
+
+    Case 3: Elementwise multiplication between sparse and diagonal matrix
+
+    >>> D = diag(torch.arange(2, 5), shape=A.shape)
+    >>> A * D
+    SparseMatrix(indices=tensor([[2],
+            [2]]),
+    values=tensor([120]),
+    shape=(3, 4), nnz=1)
     """
+    B = B.as_sparse() if isinstance(B, DiagMatrix) else B
     if isinstance(A, SparseMatrix) and isinstance(B, SparseMatrix):
-        assert A.shape == B.shape, 'The shape of sparse matrix A {} and' \
-        ' B {} are expected to match.'.format(A.shape, B.shape)
+        assert A.shape == B.shape, (
+            "The shape of sparse matrix A {} and"
+            " B {} are expected to match.".format(A.shape, B.shape)
+        )
     A = A.adj if isinstance(A, SparseMatrix) else A
     B = B.adj if isinstance(B, SparseMatrix) else B
     C = A * B
     return SparseMatrix(C.indices()[0], C.indices()[1], C.values(), C.shape)
 
-def div(A, B):
+
+def div(
+    A: Union[SparseMatrix, DiagMatrix],
+    B: Union[SparseMatrix, DiagMatrix, float],
+) -> SparseMatrix:
     """Elementwise division.
 
     Parameters
     ----------
-    A : SparseMatrix
-        Sparse matrix
-    B : SparseMatrix or scalar
-        Sparse matrix or scalar value.
+    A : SparseMatrix or DiagMatrix
+        Sparse matrix or diagonal matrix
+    B : SparseMatrix or DiagMatrix or scalar
+        Sparse matrix or diagonal matrix or scalar value.
 
     Returns
     -------
@@ -182,38 +237,46 @@ def div(A, B):
     SparseMatrix(indices=tensor([[ 0,  1,  1,  2,  7],
             [49,  0,  7,  2,  1]]),
     values=tensor([20.0000,  5.0000, 16.6667,  7.5000,  8.0000]),
-    shape=(8, 50), nnz=5)
+    shape=(10, 50), nnz=5)
 
-    Case 2: Elementwise multiplication between sparse matrix and scalar
+    Case 2: Elementwise multiplication between sparse matrix and scalar value
 
+    >>> v_scalar = 2.5
     >>> A / v_scalar
     SparseMatrix(indices=tensor([[ 0,  1,  1,  2,  7],
             [49,  0,  7,  2,  1]]),
     values=tensor([ 8.,  4., 20., 12., 16.]),
-    shape=(8, 50), nnz=5)
+    shape=(10, 50), nnz=5)
     """
+    B = B.as_sparse() if isinstance(B, DiagMatrix) else B
     if isinstance(A, SparseMatrix) and isinstance(B, SparseMatrix):
         # same sparsity structure
         if torch.equal(A.indices("COO"), B.indices("COO")):
             return SparseMatrix(A.row, A.col, A.val / B.val, A.shape)
-        raise ValueError('Division between matrices of different sparsity is not supported')
-    C = A.adj/B
+        raise ValueError(
+            "Division between matrices of different sparsity is not supported"
+        )
+    C = A.adj / B
     return SparseMatrix(C.indices()[0], C.indices()[1], C.values(), C.shape)
 
-def rdiv(A, B):
+
+def rdiv(A: float, B: Union[SparseMatrix, DiagMatrix]):
     """Elementwise division.
 
     Parameters
     ----------
     A : scalar
         scalar value
-    B : SparseMatrix
-        Sparse matrix
+    B : SparseMatrix or DiagMatrix
+        Sparse matrix or diagonal matrix
     """
-    raise RuntimeError('Elementwise division between {} and {} is not ' \
-                       'supported.'.format(type(A), type(B)))
+    raise RuntimeError(
+        "Elementwise division between {} and {} is not "
+        "supported.".format(type(A), type(B))
+    )
+
 
-def power(A, B):
+def power(A: SparseMatrix, B: float) -> SparseMatrix:
     """Elementwise power operation.
 
     Parameters
@@ -238,13 +301,16 @@ def power(A, B):
     SparseMatrix(indices=tensor([[ 0,  1,  1,  2,  7],
             [49,  0,  7,  2,  1]]),
     values=tensor([ 1788.8544,   316.2278, 17677.6699,  4929.5029, 10119.2881]),
-    shape=(8, 50), nnz=5)
+    shape=(10, 50), nnz=5)
     """
     if isinstance(B, SparseMatrix):
-        raise RuntimeError('Power operation between two sparse matrices is not supported')
-    return SparseMatrix(A.row, A.col, torch.pow(A.val, B))
+        raise RuntimeError(
+            "Power operation between two sparse matrices is not supported"
+        )
+    return SparseMatrix(A.row, A.col, torch.pow(A.val, B), A.shape)
+
 
-def rpower(A, B):
+def rpower(A: float, B: SparseMatrix) -> SparseMatrix:
     """Elementwise power operation.
 
     Parameters
@@ -254,8 +320,11 @@ def rpower(A, B):
     B : SparseMatrix
         Sparse matrix.
     """
-    raise RuntimeError('Power operation between {} and {} is not ' \
-                       'supported.'.format(type(A), type(B)))
+    raise RuntimeError(
+        "Power operation between {} and {} is not "
+        "supported.".format(type(A), type(B))
+    )
+
 
 SparseMatrix.__add__ = add
 SparseMatrix.__radd__ = add

tests/pytorch/mock_sparse/test_elementwise_op_sp.py

 import numpy as np
 import pytest
 import dgl
-import dgl.backend as F
 import torch
-import numpy
 import operator
-from dgl.mock_sparse import SparseMatrix
-parametrize_idtype = pytest.mark.parametrize("idtype", [F.int32, F.int64])
-parametrize_dtype = pytest.mark.parametrize('dtype', [F.float32, F.float64])
+from dgl.mock_sparse import SparseMatrix, diag
+
+parametrize_idtype = pytest.mark.parametrize(
+    "idtype", [torch.int32, torch.int64]
+)
+parametrize_dtype = pytest.mark.parametrize(
+    "dtype", [torch.float32, torch.float64]
+)
+
 
 def all_close_sparse(A, B):
     assert torch.allclose(A.indices(), B.indices())
     assert torch.allclose(A.values(), B.values())
     assert A.shape == B.shape
 
+
 @parametrize_idtype
 @parametrize_dtype
-@pytest.mark.parametrize('op', [operator.add, operator.sub, operator.mul, operator.truediv])
+@pytest.mark.parametrize(
+    "op", [operator.add, operator.sub, operator.mul, operator.truediv]
+)
 def test_sparse_op_sparse(idtype, dtype, op):
     rowA = torch.tensor([1, 0, 2, 7, 1])
     colA = torch.tensor([0, 49, 2, 1, 7])
@@ -36,12 +43,43 @@ def test_sparse_op_sparse(idtype, dtype, op):
             all_close_sparse(op(A.adj, B.adj), op(A, B).adj)
         else:
             # sparse div is not supported in PyTorch
-            assert np.allclose(op(A, A1).val, op(A.val, A1.val), rtol=1e-4, atol=1e-4)
+            assert np.allclose(
+                op(A, A1).val, op(A.val, A1.val), rtol=1e-4, atol=1e-4
+            )
+
+    _test()
+
+
+@parametrize_idtype
+@parametrize_dtype
+@pytest.mark.parametrize(
+    "op", [operator.add, operator.sub, operator.mul, operator.truediv]
+)
+def test_sparse_op_diag(idtype, dtype, op):
+    rowA = torch.tensor([1, 0, 2, 7, 1])
+    colA = torch.tensor([0, 49, 2, 1, 7])
+    valA = torch.rand(len(rowA))
+    A = SparseMatrix(rowA, colA, valA, shape=(10, 50))
+    D = diag(torch.arange(2, 12), shape=A.shape)
+    D_sp = D.as_sparse()
+
+    def _test():
+        if op is not operator.truediv:
+            all_close_sparse(op(A.adj, D_sp.adj), op(A, D).adj)
+        else:
+            # NOTE (Israt): Matrices mush have same sparsity pattern for div
+            D2 = diag(torch.arange(12, 22), shape=A.shape)
+            A_sp2 = D2.as_sparse()
+            assert np.allclose(
+                op(A_sp2, D).val, op(A_sp2.val, D_sp.val), rtol=1e-4, atol=1e-4
+            )
+
     _test()
 
+
 @parametrize_idtype
 @parametrize_dtype
-@pytest.mark.parametrize('v_scalar', [2, 2.5])
+@pytest.mark.parametrize("v_scalar", [2, 2.5])
 def test_sparse_op_scalar(idtype, dtype, v_scalar):
     row = torch.randint(1, 500, (100,))
     col = torch.randint(1, 500, (100,))
@@ -51,9 +89,10 @@ def test_sparse_op_scalar(idtype, dtype, v_scalar):
     all_close_sparse(A.adj / v_scalar, (A / v_scalar).adj)
     all_close_sparse(pow(A.adj, v_scalar), pow(A, v_scalar).adj)
 
+
 @parametrize_idtype
 @parametrize_dtype
-@pytest.mark.parametrize('v_scalar', [2, 2.5])
+@pytest.mark.parametrize("v_scalar", [2, 2.5])
 def test_scalar_op_sparse(idtype, dtype, v_scalar):
     row = torch.randint(1, 500, (100,))
     col = torch.randint(1, 500, (100,))
@@ -61,6 +100,7 @@ def test_scalar_op_sparse(idtype, dtype, v_scalar):
     A = SparseMatrix(row, col, val)
     all_close_sparse(v_scalar * A.adj, (v_scalar * A).adj)
 
+
 def test_expose_op():
     rowA = torch.tensor([1, 0, 2, 7, 1])
     colA = torch.tensor([0, 49, 2, 1, 7])
@@ -69,9 +109,3 @@ def test_expose_op():
     dgl.mock_sparse.sub(A, A)
     dgl.mock_sparse.mul(A, A)
     dgl.mock_sparse.div(A, A)
-
-if __name__ == '__main__':
-    test_sparse_op_sparse()
-    test_sparse_op_scalar()
-    test_scalar_op_sparse()
-    test_expose_op()