[Sparse] Polish sparse matrix creation functions (#5093)

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Co-authored-by: Ubuntu <ubuntu@ip-172-31-36-188.ap-northeast-1.compute.internal>

python/dgl/sparse/diag_matrix.py

@@ -3,7 +3,7 @@ from typing import Optional, Tuple
 
 import torch
 
-from .sparse_matrix import create_from_coo, SparseMatrix
+from .sparse_matrix import from_coo, SparseMatrix
 
 
 class DiagMatrix:
@@ -119,7 +119,7 @@ class DiagMatrix:
                      shape=(5, 5), nnz=5)
         """
         row = col = torch.arange(len(self.val)).to(self.device)
-        return create_from_coo(row=row, col=col, val=self.val, shape=self.shape)
+        return from_coo(row=row, col=col, val=self.val, shape=self.shape)
 
     def dense(self) -> torch.Tensor:
         """Return a dense representation of the matrix.

python/dgl/sparse/elementwise_op.py

@@ -30,7 +30,7 @@ def add(
     >>> row = torch.tensor([1, 0, 2])
     >>> col = torch.tensor([0, 1, 2])
     >>> val = torch.tensor([10, 20, 30])
-    >>> A = create_from_coo(row, col, val)
+    >>> A = from_coo(row, col, val)
     >>> B = diag(torch.arange(1, 4))
     >>> A + B
     SparseMatrix(indices=tensor([[0, 0, 1, 1, 2],
@@ -65,7 +65,7 @@ def power(
     >>> row = torch.tensor([1, 0, 2])
     >>> col = torch.tensor([0, 3, 2])
     >>> val = torch.tensor([10, 20, 30])
-    >>> A = create_from_coo(row, col, val)
+    >>> A = from_coo(row, col, val)
     >>> power(A, 2)
     SparseMatrix(indices=tensor([[1, 0, 2],
             [0, 3, 2]]),

python/dgl/sparse/elementwise_op_sp.py

@@ -37,7 +37,7 @@ def sp_add(A: SparseMatrix, B: Union[DiagMatrix, SparseMatrix]) -> SparseMatrix:
     >>> row = torch.tensor([1, 0, 2])
     >>> col = torch.tensor([0, 3, 2])
     >>> val = torch.tensor([10, 20, 30])
-    >>> A = create_from_coo(row, col, val, shape=(3, 4))
+    >>> A = from_coo(row, col, val, shape=(3, 4))
     >>> A + A
     SparseMatrix(indices=tensor([[0, 1, 2],
             [3, 0, 2]]),
@@ -77,7 +77,7 @@ def sp_mul(
     >>> row = torch.tensor([1, 0, 2])
     >>> col = torch.tensor([0, 3, 2])
     >>> val = torch.tensor([1, 2, 3])
-    >>> A = create_from_coo(row, col, val, shape=(3, 4))
+    >>> A = from_coo(row, col, val, shape=(3, 4))
 
     >>> A * 2
     SparseMatrix(indices=tensor([[1, 0, 2],
@@ -122,7 +122,7 @@ def sp_power(A: SparseMatrix, scalar: Union[float, int]) -> SparseMatrix:
     >>> row = torch.tensor([1, 0, 2])
     >>> col = torch.tensor([0, 3, 2])
     >>> val = torch.tensor([10, 20, 30])
-    >>> A = create_from_coo(row, col, val)
+    >>> A = from_coo(row, col, val)
     >>> A ** 2
     SparseMatrix(indices=tensor([[1, 0, 2],
             [0, 3, 2]]),

python/dgl/sparse/matmul.py

@@ -32,7 +32,7 @@ def spmm(A: Union[SparseMatrix, DiagMatrix], X: torch.Tensor) -> torch.Tensor:
     >>> row = torch.tensor([0, 1, 1])
     >>> col = torch.tensor([1, 0, 1])
     >>> val = torch.randn(len(row))
-    >>> A = create_from_coo(row, col, val)
+    >>> A = from_coo(row, col, val)
     >>> X = torch.randn(2, 3)
     >>> result = dgl.sparse.spmm(A, X)
     >>> print(type(result))
@@ -74,7 +74,7 @@ def bspmm(A: Union[SparseMatrix, DiagMatrix], X: torch.Tensor) -> torch.Tensor:
     >>> row = torch.tensor([0, 1, 1])
     >>> col = torch.tensor([1, 0, 2])
     >>> val = torch.randn(len(row), 2)
-    >>> A = create_from_coo(row, col, val, shape=(3, 3))
+    >>> A = from_coo(row, col, val, shape=(3, 3))
     >>> X = torch.randn(3, 3, 2)
     >>> result = dgl.sparse.bspmm(A, X)
     >>> print(type(result))
@@ -142,12 +142,12 @@ def spspmm(
     >>> row1 = torch.tensor([0, 1, 1])
     >>> col1 = torch.tensor([1, 0, 1])
     >>> val1 = torch.ones(len(row1))
-    >>> A1 = create_from_coo(row1, col1, val1)
+    >>> A1 = from_coo(row1, col1, val1)
 
     >>> row2 = torch.tensor([0, 1, 1])
     >>> col2 = torch.tensor([0, 2, 1])
     >>> val2 = torch.ones(len(row2))
-    >>> A2 = create_from_coo(row2, col2, val2)
+    >>> A2 = from_coo(row2, col2, val2)
     >>> result = dgl.sparse.spspmm(A1, A2)
     >>> print(result)
     SparseMatrix(indices=tensor([[0, 0, 1, 1, 1],

python/dgl/sparse/reduction.py

@@ -42,7 +42,7 @@ def reduce(A: SparseMatrix, dim: Optional[int] = None, rtype: str = "sum"):
     >>> row = torch.tensor([0, 1, 1])
     >>> col = torch.tensor([0, 0, 2])
     >>> val = torch.tensor([1, 1, 2])
-    >>> A = dglsp.create_from_coo(row, col, val, shape=(4, 3))
+    >>> A = dglsp.from_coo(row, col, val, shape=(4, 3))
     >>> print(A.reduce(rtype='sum'))
     tensor(4)
     >>> print(A.reduce(0, 'sum'))
@@ -57,7 +57,7 @@ def reduce(A: SparseMatrix, dim: Optional[int] = None, rtype: str = "sum"):
     >>> row = torch.tensor([0, 1, 1])
     >>> col = torch.tensor([0, 0, 2])
     >>> val = torch.tensor([[1., 2.], [2., 1.], [2., 2.]])
-    >>> A = dglsp.create_from_coo(row, col, val, shape=(4, 3))
+    >>> A = dglsp.from_coo(row, col, val, shape=(4, 3))
     >>> print(A.reduce(rtype='sum'))
     tensor([5., 5.])
     >>> print(A.reduce(0, 'sum'))
@@ -105,7 +105,7 @@ def sum(A: SparseMatrix, dim: Optional[int] = None):
     >>> row = torch.tensor([0, 1, 1])
     >>> col = torch.tensor([0, 0, 2])
     >>> val = torch.tensor([1, 1, 2])
-    >>> A = dglsp.create_from_coo(row, col, val, shape=(4, 3))
+    >>> A = dglsp.from_coo(row, col, val, shape=(4, 3))
     >>> print(A.sum())
     tensor(4)
     >>> print(A.sum(0))
@@ -116,7 +116,7 @@ def sum(A: SparseMatrix, dim: Optional[int] = None):
     >>> row = torch.tensor([0, 1, 1])
     >>> col = torch.tensor([0, 0, 2])
     >>> val = torch.tensor([[1, 2], [2, 1], [2, 2]])
-    >>> A = dglsp.create_from_coo(row, col, val, shape=(4, 3))
+    >>> A = dglsp.from_coo(row, col, val, shape=(4, 3))
     >>> print(A.sum())
     tensor([5, 5])
     >>> print(A.sum(0))
@@ -157,7 +157,7 @@ def smax(A: SparseMatrix, dim: Optional[int] = None):
     >>> row = torch.tensor([0, 1, 1])
     >>> col = torch.tensor([0, 0, 2])
     >>> val = torch.tensor([1, 1, 2])
-    >>> A = dglsp.create_from_coo(row, col, val, shape=(4, 3))
+    >>> A = dglsp.from_coo(row, col, val, shape=(4, 3))
     >>> print(A.smax())
     tensor(2)
     >>> print(A.smax(0))
@@ -168,7 +168,7 @@ def smax(A: SparseMatrix, dim: Optional[int] = None):
     >>> row = torch.tensor([0, 1, 1])
     >>> col = torch.tensor([0, 0, 2])
     >>> val = torch.tensor([[1, 2], [2, 1], [2, 2]])
-    >>> A = dglsp.create_from_coo(row, col, val, shape=(4, 3))
+    >>> A = dglsp.from_coo(row, col, val, shape=(4, 3))
     >>> print(A.smax())
     tensor([2, 2])
     >>> print(A.smax(1))
@@ -210,7 +210,7 @@ def smin(A: SparseMatrix, dim: Optional[int] = None):
     >>> row = torch.tensor([0, 1, 1])
     >>> col = torch.tensor([0, 0, 2])
     >>> val = torch.tensor([1, 1, 2])
-    >>> A = dglsp.create_from_coo(row, col, val, shape=(4, 3))
+    >>> A = dglsp.from_coo(row, col, val, shape=(4, 3))
     >>> print(A.smin())
     tensor(1)
     >>> print(A.smin(0))
@@ -221,7 +221,7 @@ def smin(A: SparseMatrix, dim: Optional[int] = None):
     >>> row = torch.tensor([0, 1, 1])
     >>> col = torch.tensor([0, 0, 2])
     >>> val = torch.tensor([[1, 2], [2, 1], [2, 2]])
-    >>> A = dglsp.create_from_coo(row, col, val, shape=(4, 3))
+    >>> A = dglsp.from_coo(row, col, val, shape=(4, 3))
     >>> print(A.smin())
     tensor([1, 1])
     >>> print(A.smin(0))
@@ -267,7 +267,7 @@ def smean(A: SparseMatrix, dim: Optional[int] = None):
     >>> row = torch.tensor([0, 1, 1])
     >>> col = torch.tensor([0, 0, 2])
     >>> val = torch.tensor([1., 1., 2.])
-    >>> A = dglsp.create_from_coo(row, col, val, shape=(4, 3))
+    >>> A = dglsp.from_coo(row, col, val, shape=(4, 3))
     >>> print(A.smean())
     tensor(1.3333)
     >>> print(A.smean(0))
@@ -278,7 +278,7 @@ def smean(A: SparseMatrix, dim: Optional[int] = None):
     >>> row = torch.tensor([0, 1, 1])
     >>> col = torch.tensor([0, 0, 2])
     >>> val = torch.tensor([[1., 2.], [2., 1.], [2., 2.]])
-    >>> A = dglsp.create_from_coo(row, col, val, shape=(4, 3))
+    >>> A = dglsp.from_coo(row, col, val, shape=(4, 3))
     >>> print(A.smean())
     tensor([1.6667, 1.6667])
     >>> print(A.smean(0))
@@ -324,7 +324,7 @@ def sprod(A: SparseMatrix, dim: Optional[int] = None):
     >>> row = torch.tensor([0, 1, 1])
     >>> col = torch.tensor([0, 0, 2])
     >>> val = torch.tensor([1, 1, 2])
-    >>> A = dglsp.create_from_coo(row, col, val, shape=(4, 3))
+    >>> A = dglsp.from_coo(row, col, val, shape=(4, 3))
     >>> print(A.sprod())
     tensor(2)
     >>> print(A.sprod(0))
@@ -335,7 +335,7 @@ def sprod(A: SparseMatrix, dim: Optional[int] = None):
     >>> row = torch.tensor([0, 1, 1])
     >>> col = torch.tensor([0, 0, 2])
     >>> val = torch.tensor([[1, 2], [2, 1], [2, 2]])
-    >>> A = dglsp.create_from_coo(row, col, val, shape=(4, 3))
+    >>> A = dglsp.from_coo(row, col, val, shape=(4, 3))
     >>> print(A.sprod())
     tensor([4, 4])
     >>> print(A.sprod(0))

python/dgl/sparse/sddmm.py

@@ -44,7 +44,7 @@ def sddmm(
     >>> row = torch.tensor([1, 1, 2])
     >>> col = torch.tensor([2, 3, 3])
     >>> val = torch.arange(1, 4).float()
-    >>> A = create_from_coo(row, col, val, (3, 4))
+    >>> A = from_coo(row, col, val, (3, 4))
     >>> mat1 = torch.randn(3, 5)
     >>> mat2 = torch.randn(5, 4)
     >>> dgl.sparse.sddmm(A, mat1, mat2)
@@ -96,7 +96,7 @@ def bsddmm(
     >>> row = torch.tensor([1, 1, 2])
     >>> col = torch.tensor([2, 3, 3])
     >>> val = torch.arange(1, 4).float()
-    >>> A = create_from_coo(row, col, val, (3, 4))
+    >>> A = from_coo(row, col, val, (3, 4))
     >>> mat1 = torch.arange(0, 3 * 5 * 2).view(3, 5, 2).float()
     >>> mat2 = torch.arange(0, 5 * 4 * 2).view(5, 4, 2).float()
     >>> dgl.sparse.bsddmm(A, mat1, mat2)

python/dgl/sparse/softmax.py

@@ -35,7 +35,7 @@ def softmax(A: SparseMatrix) -> SparseMatrix:
     >>> col = torch.tensor([1, 2, 2, 0])
     >>> nnz = len(row)
     >>> val = torch.arange(nnz).float()
-    >>> A = create_from_coo(row, col, val)
+    >>> A = from_coo(row, col, val)
     >>> softmax(A)
     SparseMatrix(indices=tensor([[0, 0, 1, 2],
         [1, 2, 2, 0]]),
@@ -45,7 +45,7 @@ def softmax(A: SparseMatrix) -> SparseMatrix:
     Case2: matrix with values of shape (nnz, D)
 
     >>> val = torch.tensor([[0., 7.], [1., 3.], [2., 2.], [3., 1.]])
-    >>> A = create_from_coo(row, col, val)
+    >>> A = from_coo(row, col, val)
     >>> softmax(A)
     SparseMatrix(indices=tensor([[0, 0, 1, 2],
         [1, 2, 2, 0]]),

python/dgl/sparse/sparse_matrix.py

@@ -184,7 +184,7 @@ class SparseMatrix:
         >>> row = torch.tensor([1, 1, 3])
         >>> col = torch.tensor([2, 1, 3])
         >>> val = torch.tensor([1, 1, 2])
-        >>> A = create_from_coo(row, col, val)
+        >>> A = from_coo(row, col, val)
         >>> A = A.transpose()
         >>> print(A)
         SparseMatrix(indices=tensor([[2, 1, 3],
@@ -217,7 +217,7 @@ class SparseMatrix:
         >>> row = torch.tensor([1, 0, 0, 0, 1])
         >>> col = torch.tensor([1, 1, 1, 2, 2])
         >>> val = torch.tensor([0, 1, 2, 3, 4])
-        >>> A = create_from_coo(row, col, val)
+        >>> A = from_coo(row, col, val)
         >>> A = A.coalesce()
         >>> print(A)
         SparseMatrix(indices=tensor([[0, 0, 1, 1],
@@ -240,7 +240,7 @@ class SparseMatrix:
         >>> row = torch.tensor([1, 0, 0, 0, 1])
         >>> col = torch.tensor([1, 1, 1, 2, 2])
         >>> val = torch.tensor([0, 1, 2, 3, 4])
-        >>> A = create_from_coo(row, col, val)
+        >>> A = from_coo(row, col, val)
         >>> print(A.has_duplicate())
         True
         >>> print(A.coalesce().has_duplicate())
@@ -249,7 +249,7 @@ class SparseMatrix:
         return self.c_sparse_matrix.has_duplicate()
 
 
-def create_from_coo(
+def from_coo(
     row: torch.Tensor,
     col: torch.Tensor,
     val: Optional[torch.Tensor] = None,
@@ -283,14 +283,14 @@ def create_from_coo(
 
     >>> dst = torch.tensor([1, 1, 2])
     >>> src = torch.tensor([2, 4, 3])
-    >>> A = create_from_coo(dst, src)
+    >>> A = from_coo(dst, src)
     >>> print(A)
     SparseMatrix(indices=tensor([[1, 1, 2],
             [2, 4, 3]]),
     values=tensor([1., 1., 1.]),
     shape=(3, 5), nnz=3)
     >>> # Specify shape
-    >>> A = create_from_coo(dst, src, shape=(5, 5))
+    >>> A = from_coo(dst, src, shape=(5, 5))
     >>> print(A)
     SparseMatrix(indices=tensor([[1, 1, 2],
             [2, 4, 3]]),
@@ -301,7 +301,7 @@ def create_from_coo(
     vector data.
 
     >>> val = torch.tensor([[1., 1.], [2., 2.], [3., 3.]])
-    >>> A = create_from_coo(dst, src, val)
+    >>> A = from_coo(dst, src, val)
     SparseMatrix(indices=tensor([[1, 1, 2],
             [2, 4, 3]]),
     values=tensor([[1., 1.],
@@ -314,12 +314,10 @@ def create_from_coo(
     if val is None:
         val = torch.ones(row.shape[0]).to(row.device)
 
-    return SparseMatrix(
-        torch.ops.dgl_sparse.create_from_coo(row, col, val, shape)
-    )
+    return SparseMatrix(torch.ops.dgl_sparse.from_coo(row, col, val, shape))
 
 
-def create_from_csr(
+def from_csr(
     indptr: torch.Tensor,
     indices: torch.Tensor,
     val: Optional[torch.Tensor] = None,
@@ -364,14 +362,14 @@ def create_from_csr(
 
     >>> indptr = torch.tensor([0, 1, 2, 5])
     >>> indices = torch.tensor([1, 2, 0, 1, 2])
-    >>> A = create_from_csr(indptr, indices)
+    >>> A = from_csr(indptr, indices)
     >>> print(A)
     SparseMatrix(indices=tensor([[0, 1, 2, 2, 2],
             [1, 2, 0, 1, 2]]),
     values=tensor([1., 1., 1., 1., 1.]),
     shape=(3, 3), nnz=5)
     >>> # Specify shape
-    >>> A = create_from_csr(indptr, indices, shape=(3, 5))
+    >>> A = from_csr(indptr, indices, shape=(3, 5))
     >>> print(A)
     SparseMatrix(indices=tensor([[0, 1, 2, 2, 2],
             [1, 2, 0, 1, 2]]),
@@ -382,7 +380,7 @@ def create_from_csr(
     vector data.
 
     >>> val = torch.tensor([[1, 1], [2, 2], [3, 3], [4, 4], [5, 5]])
-    >>> A = create_from_csr(indptr, indices, val)
+    >>> A = from_csr(indptr, indices, val)
     >>> print(A)
     SparseMatrix(indices=tensor([[0, 1, 2, 2, 2],
             [1, 2, 0, 1, 2]]),
@@ -399,11 +397,11 @@ def create_from_csr(
         val = torch.ones(indices.shape[0]).to(indptr.device)
 
     return SparseMatrix(
-        torch.ops.dgl_sparse.create_from_csr(indptr, indices, val, shape)
+        torch.ops.dgl_sparse.from_csr(indptr, indices, val, shape)
     )
 
 
-def create_from_csc(
+def from_csc(
     indptr: torch.Tensor,
     indices: torch.Tensor,
     val: Optional[torch.Tensor] = None,
@@ -448,14 +446,14 @@ def create_from_csc(
 
     >>> indptr = torch.tensor([0, 1, 3, 5])
     >>> indices = torch.tensor([2, 0, 2, 1, 2])
-    >>> A = create_from_csc(indptr, indices)
+    >>> A = from_csc(indptr, indices)
     >>> print(A)
     SparseMatrix(indices=tensor([[2, 0, 2, 1, 2],
             [0, 1, 1, 2, 2]]),
     values=tensor([1., 1., 1., 1., 1.]),
     shape=(3, 3), nnz=5)
     >>> # Specify shape
-    >>> A = create_from_csc(indptr, indices, shape=(5, 3))
+    >>> A = from_csc(indptr, indices, shape=(5, 3))
     >>> print(A)
     SparseMatrix(indices=tensor([[2, 0, 2, 1, 2],
             [0, 1, 1, 2, 2]]),
@@ -466,7 +464,7 @@ def create_from_csc(
     vector data.
 
     >>> val = torch.tensor([[1, 1], [2, 2], [3, 3], [4, 4], [5, 5]])
-    >>> A = create_from_csc(indptr, indices, val)
+    >>> A = from_csc(indptr, indices, val)
     >>> print(A)
     SparseMatrix(indices=tensor([[2, 0, 2, 1, 2],
             [0, 1, 1, 2, 2]]),
@@ -483,7 +481,7 @@ def create_from_csc(
         val = torch.ones(indices.shape[0]).to(indptr.device)
 
     return SparseMatrix(
-        torch.ops.dgl_sparse.create_from_csc(indptr, indices, val, shape)
+        torch.ops.dgl_sparse.from_csc(indptr, indices, val, shape)
     )
 
 
@@ -511,7 +509,7 @@ def val_like(mat: SparseMatrix, val: torch.Tensor) -> SparseMatrix:
     >>> row = torch.tensor([1, 1, 2])
     >>> col = torch.tensor([2, 4, 3])
     >>> val = torch.ones(3)
-    >>> A = create_from_coo(row, col, val)
+    >>> A = from_coo(row, col, val)
     >>> B = val_like(A, torch.tensor([2, 2, 2]))
     >>> print(B)
     SparseMatrix(indices=tensor([[1, 1, 2],

python/dgl/sparse/unary_op_sp.py

@@ -17,7 +17,7 @@ def neg(A: SparseMatrix) -> SparseMatrix:
     >>> row = torch.tensor([1, 1, 3])
     >>> col = torch.tensor([1, 2, 3])
     >>> val = torch.tensor([1., 1., 2.])
-    >>> A = create_from_coo(row, col, val)
+    >>> A = from_coo(row, col, val)
     >>> A = -A
     >>> print(A)
     SparseMatrix(indices=tensor([[1, 1, 3],

tests/pytorch/sparse/test_elementwise_op.py

@@ -4,13 +4,7 @@ import backend as F
 import pytest
 import torch
 
-from dgl.sparse import (
-    add,
-    create_from_coo,
-    create_from_csc,
-    create_from_csr,
-    diag,
-)
+from dgl.sparse import add, diag, from_coo, from_csc, from_csr
 
 # TODO(#4818): Skipping tests on win.
 if not sys.platform.startswith("linux"):
@@ -23,12 +17,12 @@ def test_add_coo(val_shape):
     row = torch.tensor([1, 0, 2]).to(ctx)
     col = torch.tensor([0, 3, 2]).to(ctx)
     val = torch.randn(row.shape + val_shape).to(ctx)
-    A = create_from_coo(row, col, val)
+    A = from_coo(row, col, val)
 
     row = torch.tensor([1, 0]).to(ctx)
     col = torch.tensor([0, 2]).to(ctx)
     val = torch.randn(row.shape + val_shape).to(ctx)
-    B = create_from_coo(row, col, val, shape=A.shape)
+    B = from_coo(row, col, val, shape=A.shape)
 
     sum1 = (A + B).dense()
     sum2 = add(A, B).dense()
@@ -44,12 +38,12 @@ def test_add_csr(val_shape):
     indptr = torch.tensor([0, 1, 2, 3]).to(ctx)
     indices = torch.tensor([3, 0, 2]).to(ctx)
     val = torch.randn(indices.shape + val_shape).to(ctx)
-    A = create_from_csr(indptr, indices, val)
+    A = from_csr(indptr, indices, val)
 
     indptr = torch.tensor([0, 1, 2, 2]).to(ctx)
     indices = torch.tensor([2, 0]).to(ctx)
     val = torch.randn(indices.shape + val_shape).to(ctx)
-    B = create_from_csr(indptr, indices, val, shape=A.shape)
+    B = from_csr(indptr, indices, val, shape=A.shape)
 
     sum1 = (A + B).dense()
     sum2 = add(A, B).dense()
@@ -65,12 +59,12 @@ def test_add_csc(val_shape):
     indptr = torch.tensor([0, 1, 1, 2, 3]).to(ctx)
     indices = torch.tensor([1, 2, 0]).to(ctx)
     val = torch.randn(indices.shape + val_shape).to(ctx)
-    A = create_from_csc(indptr, indices, val)
+    A = from_csc(indptr, indices, val)
 
     indptr = torch.tensor([0, 1, 1, 2, 2]).to(ctx)
     indices = torch.tensor([1, 0]).to(ctx)
     val = torch.randn(indices.shape + val_shape).to(ctx)
-    B = create_from_csc(indptr, indices, val, shape=A.shape)
+    B = from_csc(indptr, indices, val, shape=A.shape)
 
     sum1 = (A + B).dense()
     sum2 = add(A, B).dense()
@@ -102,7 +96,7 @@ def test_add_sparse_diag(val_shape):
     row = torch.tensor([1, 0, 2]).to(ctx)
     col = torch.tensor([0, 3, 2]).to(ctx)
     val = torch.randn(row.shape + val_shape).to(ctx)
-    A = create_from_coo(row, col, val)
+    A = from_coo(row, col, val)
 
     shape = (3, 4)
     val_shape = (shape[0],) + val_shape

tests/pytorch/sparse/test_elementwise_op_sp.py

@@ -4,7 +4,7 @@ import backend as F
 import pytest
 import torch
 
-from dgl.sparse import create_from_coo, power
+from dgl.sparse import from_coo, power
 
 # TODO(#4818): Skipping tests on win.
 if not sys.platform.startswith("linux"):
@@ -26,7 +26,7 @@ def test_mul_scalar(v_scalar):
     row = torch.tensor([1, 0, 2]).to(ctx)
     col = torch.tensor([0, 3, 2]).to(ctx)
     val = torch.randn(len(row)).to(ctx)
-    A1 = create_from_coo(row, col, val, shape=(3, 4))
+    A1 = from_coo(row, col, val, shape=(3, 4))
 
     # A * v
     A2 = A1 * v_scalar
@@ -46,7 +46,7 @@ def test_pow(val_shape):
     row = torch.tensor([1, 0, 2]).to(ctx)
     col = torch.tensor([0, 3, 2]).to(ctx)
     val = torch.randn(val_shape).to(ctx)
-    A = create_from_coo(row, col, val, shape=(3, 4))
+    A = from_coo(row, col, val, shape=(3, 4))
     exponent = 2
     A_new = A**exponent
     assert torch.allclose(A_new.val, val**exponent)

tests/pytorch/sparse/test_matmul.py

@@ -4,7 +4,7 @@ import backend as F
 import pytest
 import torch
 
-from dgl.sparse import bspmm, create_from_coo, val_like
+from dgl.sparse import bspmm, from_coo, val_like
 
 from .utils import (
     clone_detach_and_grad,
@@ -123,13 +123,13 @@ def test_spspmm_duplicate():
     col = torch.tensor([1, 1, 1, 2, 2]).to(dev)
     val = torch.randn(len(row)).to(dev)
     shape = (4, 4)
-    A1 = create_from_coo(row, col, val, shape)
+    A1 = from_coo(row, col, val, shape)
 
     row = torch.tensor([1, 0, 0, 1]).to(dev)
     col = torch.tensor([1, 1, 2, 2]).to(dev)
     val = torch.randn(len(row)).to(dev)
     shape = (4, 4)
-    A2 = create_from_coo(row, col, val, shape)
+    A2 = from_coo(row, col, val, shape)
 
     try:
         A1 @ A2

tests/pytorch/sparse/test_reduction.py

@@ -93,7 +93,7 @@ def test_reduce_all(shape, op, use_reduce):
     val2 = val.clone()
     val = val.requires_grad_()
     val2 = val2.requires_grad_()
-    A = dglsp.create_from_coo(row, col, val, shape=(NUM_ROWS, NUM_COLS))
+    A = dglsp.from_coo(row, col, val, shape=(NUM_ROWS, NUM_COLS))
 
     A2, M = _coalesce_dense(row, col, val2, NUM_ROWS, NUM_COLS, op)
 
@@ -139,7 +139,7 @@ def test_reduce_along(shape, dim, empty_nnz, op, use_reduce):
         row[row == 0] = 1
         col[col == 0] = 1
 
-    A = dglsp.create_from_coo(row, col, val, shape=(NUM_ROWS, NUM_COLS))
+    A = dglsp.from_coo(row, col, val, shape=(NUM_ROWS, NUM_COLS))
 
     A2, M = _coalesce_dense(row, col, val2, NUM_ROWS, NUM_COLS, op)
 

tests/pytorch/sparse/test_softmax.py

@@ -6,7 +6,7 @@ import dgl
 import pytest
 import torch
 
-from dgl.sparse import create_from_coo, softmax
+from dgl.sparse import from_coo, softmax
 
 # TODO(#4818): Skipping tests on win.
 if not sys.platform.startswith("linux"):
@@ -26,7 +26,7 @@ def test_softmax(val_D, csr):
         val = torch.randn(nnz, val_D).to(dev)
 
     val_sparse = val.clone().requires_grad_()
-    A = create_from_coo(row, col, val_sparse)
+    A = from_coo(row, col, val_sparse)
 
     if csr:
         # Test CSR

tests/pytorch/sparse/test_sparse_matrix.py

@@ -4,12 +4,7 @@ import backend as F
 import pytest
 import torch
 
-from dgl.sparse import (
-    create_from_coo,
-    create_from_csc,
-    create_from_csr,
-    val_like,
-)
+from dgl.sparse import from_coo, from_csc, from_csr, val_like
 
 # TODO(#4818): Skipping tests on win.
 if not sys.platform.startswith("linux"):
@@ -20,7 +15,7 @@ if not sys.platform.startswith("linux"):
 @pytest.mark.parametrize("row", [(0, 0, 1, 2), (0, 1, 2, 4)])
 @pytest.mark.parametrize("col", [(0, 1, 2, 2), (1, 3, 3, 4)])
 @pytest.mark.parametrize("shape", [None, (5, 5), (5, 6)])
-def test_create_from_coo(dense_dim, row, col, shape):
+def test_from_coo(dense_dim, row, col, shape):
     val_shape = (len(row),)
     if dense_dim is not None:
         val_shape += (dense_dim,)
@@ -28,7 +23,7 @@ def test_create_from_coo(dense_dim, row, col, shape):
     val = torch.randn(val_shape).to(ctx)
     row = torch.tensor(row).to(ctx)
     col = torch.tensor(col).to(ctx)
-    mat = create_from_coo(row, col, val, shape)
+    mat = from_coo(row, col, val, shape)
 
     if shape is None:
         shape = (torch.max(row).item() + 1, torch.max(col).item() + 1)
@@ -48,7 +43,7 @@ def test_create_from_coo(dense_dim, row, col, shape):
 @pytest.mark.parametrize("indptr", [(0, 0, 1, 4), (0, 1, 2, 4)])
 @pytest.mark.parametrize("indices", [(0, 1, 2, 3), (1, 2, 3, 4)])
 @pytest.mark.parametrize("shape", [None, (3, 5)])
-def test_create_from_csr(dense_dim, indptr, indices, shape):
+def test_from_csr(dense_dim, indptr, indices, shape):
     val_shape = (len(indices),)
     if dense_dim is not None:
         val_shape += (dense_dim,)
@@ -56,7 +51,7 @@ def test_create_from_csr(dense_dim, indptr, indices, shape):
     val = torch.randn(val_shape).to(ctx)
     indptr = torch.tensor(indptr).to(ctx)
     indices = torch.tensor(indices).to(ctx)
-    mat = create_from_csr(indptr, indices, val, shape)
+    mat = from_csr(indptr, indices, val, shape)
 
     if shape is None:
         shape = (indptr.numel() - 1, torch.max(indices).item() + 1)
@@ -76,7 +71,7 @@ def test_create_from_csr(dense_dim, indptr, indices, shape):
 @pytest.mark.parametrize("indptr", [(0, 0, 1, 4), (0, 1, 2, 4)])
 @pytest.mark.parametrize("indices", [(0, 1, 2, 3), (1, 2, 3, 4)])
 @pytest.mark.parametrize("shape", [None, (5, 3)])
-def test_create_from_csc(dense_dim, indptr, indices, shape):
+def test_from_csc(dense_dim, indptr, indices, shape):
     val_shape = (len(indices),)
     if dense_dim is not None:
         val_shape += (dense_dim,)
@@ -84,7 +79,7 @@ def test_create_from_csc(dense_dim, indptr, indices, shape):
     val = torch.randn(val_shape).to(ctx)
     indptr = torch.tensor(indptr).to(ctx)
     indices = torch.tensor(indices).to(ctx)
-    mat = create_from_csc(indptr, indices, val, shape)
+    mat = from_csc(indptr, indices, val, shape)
 
     if shape is None:
         shape = (torch.max(indices).item() + 1, indptr.numel() - 1)
@@ -107,7 +102,7 @@ def test_dense(val_shape):
     row = torch.tensor([1, 1, 2]).to(ctx)
     col = torch.tensor([2, 4, 3]).to(ctx)
     val = torch.randn(val_shape).to(ctx)
-    A = create_from_coo(row, col, val)
+    A = from_coo(row, col, val)
     A_dense = A.dense()
 
     shape = A.shape + val.shape[1:]
@@ -128,7 +123,7 @@ def test_csr_to_coo(dense_dim, indptr, indices, shape):
     val = torch.randn(val_shape).to(ctx)
     indptr = torch.tensor(indptr).to(ctx)
     indices = torch.tensor(indices).to(ctx)
-    mat = create_from_csr(indptr, indices, val, shape)
+    mat = from_csr(indptr, indices, val, shape)
 
     if shape is None:
         shape = (indptr.numel() - 1, torch.max(indices).item() + 1)
@@ -163,7 +158,7 @@ def test_csc_to_coo(dense_dim, indptr, indices, shape):
     val = torch.randn(val_shape).to(ctx)
     indptr = torch.tensor(indptr).to(ctx)
     indices = torch.tensor(indices).to(ctx)
-    mat = create_from_csc(indptr, indices, val, shape)
+    mat = from_csc(indptr, indices, val, shape)
 
     if shape is None:
         shape = (torch.max(indices).item() + 1, indptr.numel() - 1)
@@ -205,7 +200,7 @@ def test_coo_to_csr(dense_dim, row, col, shape):
     val = torch.randn(val_shape).to(ctx)
     row = torch.tensor(row).to(ctx)
     col = torch.tensor(col).to(ctx)
-    mat = create_from_coo(row, col, val, shape)
+    mat = from_coo(row, col, val, shape)
 
     if shape is None:
         shape = (torch.max(row).item() + 1, torch.max(col).item() + 1)
@@ -237,7 +232,7 @@ def test_csc_to_csr(dense_dim, indptr, indices, shape):
     val = torch.randn(val_shape).to(ctx)
     indptr = torch.tensor(indptr).to(ctx)
     indices = torch.tensor(indices).to(ctx)
-    mat = create_from_csc(indptr, indices, val, shape)
+    mat = from_csc(indptr, indices, val, shape)
     mat_indptr, mat_indices, value_indices = mat.csr()
     mat_val = mat.val if value_indices is None else mat.val[value_indices]
 
@@ -280,7 +275,7 @@ def test_coo_to_csc(dense_dim, row, col, shape):
     val = torch.randn(val_shape).to(ctx)
     row = torch.tensor(row).to(ctx)
     col = torch.tensor(col).to(ctx)
-    mat = create_from_coo(row, col, val, shape)
+    mat = from_coo(row, col, val, shape)
 
     if shape is None:
         shape = (torch.max(row).item() + 1, torch.max(col).item() + 1)
@@ -312,7 +307,7 @@ def test_csr_to_csc(dense_dim, indptr, indices, shape):
     val = torch.randn(val_shape).to(ctx)
     indptr = torch.tensor(indptr).to(ctx)
     indices = torch.tensor(indices).to(ctx)
-    mat = create_from_csr(indptr, indices, val, shape)
+    mat = from_csr(indptr, indices, val, shape)
     mat_indptr, mat_indices, value_indices = mat.csc()
     mat_val = mat.val if value_indices is None else mat.val[value_indices]
 
@@ -358,20 +353,20 @@ def test_val_like(val_shape, shape):
     row = torch.tensor([1, 1, 2]).to(ctx)
     col = torch.tensor([2, 4, 3]).to(ctx)
     val = torch.randn(3).to(ctx)
-    coo_A = create_from_coo(row, col, val, shape)
+    coo_A = from_coo(row, col, val, shape)
     new_val = torch.randn(val_shape).to(ctx)
     coo_B = val_like(coo_A, new_val)
     check_val_like(coo_A, coo_B)
 
     # CSR
     indptr, indices, _ = coo_A.csr()
-    csr_A = create_from_csr(indptr, indices, val, shape)
+    csr_A = from_csr(indptr, indices, val, shape)
     csr_B = val_like(csr_A, new_val)
     check_val_like(csr_A, csr_B)
 
     # CSC
     indptr, indices, _ = coo_A.csc()
-    csc_A = create_from_csc(indptr, indices, val, shape)
+    csc_A = from_csc(indptr, indices, val, shape)
     csc_B = val_like(csc_A, new_val)
     check_val_like(csc_A, csc_B)
 
@@ -382,7 +377,7 @@ def test_coalesce():
     row = torch.tensor([1, 0, 0, 0, 1]).to(ctx)
     col = torch.tensor([1, 1, 1, 2, 2]).to(ctx)
     val = torch.arange(len(row)).to(ctx)
-    A = create_from_coo(row, col, val, (4, 4))
+    A = from_coo(row, col, val, (4, 4))
 
     assert A.has_duplicate()
 
@@ -406,17 +401,17 @@ def test_has_duplicate():
     shape = (4, 4)
 
     # COO
-    coo_A = create_from_coo(row, col, val, shape)
+    coo_A = from_coo(row, col, val, shape)
     assert coo_A.has_duplicate()
 
     # CSR
     indptr, indices, _ = coo_A.csr()
-    csr_A = create_from_csr(indptr, indices, val, shape)
+    csr_A = from_csr(indptr, indices, val, shape)
     assert csr_A.has_duplicate()
 
     # CSC
     indptr, indices, _ = coo_A.csc()
-    csc_A = create_from_csc(indptr, indices, val, shape)
+    csc_A = from_csc(indptr, indices, val, shape)
     assert csc_A.has_duplicate()
 
 
@@ -427,12 +422,12 @@ def test_print():
     row = torch.tensor([1, 1, 3]).to(ctx)
     col = torch.tensor([2, 1, 3]).to(ctx)
     val = torch.tensor([1.0, 1.0, 2.0]).to(ctx)
-    A = create_from_coo(row, col, val)
+    A = from_coo(row, col, val)
     print(A)
 
     # vector-shape non zero
     row = torch.tensor([1, 1, 3]).to(ctx)
     col = torch.tensor([2, 1, 3]).to(ctx)
     val = torch.randn(3, 2).to(ctx)
-    A = create_from_coo(row, col, val)
+    A = from_coo(row, col, val)
     print(A)

tests/pytorch/sparse/test_transpose.py

@@ -4,7 +4,7 @@ import backend as F
 import pytest
 import torch
 
-from dgl.sparse import create_from_coo, diag
+from dgl.sparse import diag, from_coo
 
 # TODO(#4818): Skipping tests on win.
 if not sys.platform.startswith("linux"):
@@ -37,7 +37,7 @@ def test_sparse_matrix_transpose(dense_dim, row, col, extra_shape):
     val = torch.randn(val_shape).to(ctx)
     row = torch.tensor(row).to(ctx)
     col = torch.tensor(col).to(ctx)
-    mat = create_from_coo(row, col, val, mat_shape).transpose()
+    mat = from_coo(row, col, val, mat_shape).transpose()
     mat_row, mat_col = mat.coo()
     mat_val = mat.val
 

tests/pytorch/sparse/test_unary_op_sp.py

@@ -4,7 +4,7 @@ import backend as F
 import pytest
 import torch
 
-from dgl.sparse import create_from_coo
+from dgl.sparse import from_coo
 
 # TODO(#4818): Skipping tests on win.
 if not sys.platform.startswith("linux"):
@@ -16,7 +16,7 @@ def test_neg():
     row = torch.tensor([1, 1, 3]).to(ctx)
     col = torch.tensor([1, 2, 3]).to(ctx)
     val = torch.tensor([1.0, 1.0, 2.0]).to(ctx)
-    A = create_from_coo(row, col, val)
+    A = from_coo(row, col, val)
     neg_A = -A
     assert A.shape == neg_A.shape
     assert A.nnz == neg_A.nnz

tests/pytorch/sparse/utils.py

 import numpy as np
 import torch
 
-from dgl.sparse import (
-    create_from_coo,
-    create_from_csc,
-    create_from_csr,
-    SparseMatrix,
-)
+from dgl.sparse import from_coo, from_csc, from_csr, SparseMatrix
 
 np.random.seed(42)
 torch.random.manual_seed(42)
@@ -28,7 +23,7 @@ def rand_coo(shape, nnz, dev, nz_dim=None):
         val = torch.randn(nnz, device=dev, requires_grad=True)
     else:
         val = torch.randn(nnz, nz_dim, device=dev, requires_grad=True)
-    return create_from_coo(row, col, val, shape)
+    return from_coo(row, col, val, shape)
 
 
 def rand_csr(shape, nnz, dev, nz_dim=None):
@@ -47,7 +42,7 @@ def rand_csr(shape, nnz, dev, nz_dim=None):
     indptr = torch.cumsum(indptr, 0)
     row_sorted, row_sorted_idx = torch.sort(row)
     indices = col[row_sorted_idx]
-    return create_from_csr(indptr, indices, val, shape=shape)
+    return from_csr(indptr, indices, val, shape=shape)
 
 
 def rand_csc(shape, nnz, dev, nz_dim=None):
@@ -66,7 +61,7 @@ def rand_csc(shape, nnz, dev, nz_dim=None):
     indptr = torch.cumsum(indptr, 0)
     col_sorted, col_sorted_idx = torch.sort(col)
     indices = row[col_sorted_idx]
-    return create_from_csc(indptr, indices, val, shape=shape)
+    return from_csc(indptr, indices, val, shape=shape)
 
 
 def rand_coo_uncoalesced(shape, nnz, dev):
@@ -74,7 +69,7 @@ def rand_coo_uncoalesced(shape, nnz, dev):
     row = torch.randint(shape[0], (nnz,), device=dev)
     col = torch.randint(shape[1], (nnz,), device=dev)
     val = torch.randn(nnz, device=dev, requires_grad=True)
-    return create_from_coo(row, col, val, shape)
+    return from_coo(row, col, val, shape)
 
 
 def rand_csr_uncoalesced(shape, nnz, dev):
@@ -88,7 +83,7 @@ def rand_csr_uncoalesced(shape, nnz, dev):
     indptr = torch.cumsum(indptr, 0)
     row_sorted, row_sorted_idx = torch.sort(row)
     indices = col[row_sorted_idx]
-    return create_from_csr(indptr, indices, val, shape=shape)
+    return from_csr(indptr, indices, val, shape=shape)
 
 
 def rand_csc_uncoalesced(shape, nnz, dev):
@@ -102,7 +97,7 @@ def rand_csc_uncoalesced(shape, nnz, dev):
     indptr = torch.cumsum(indptr, 0)
     col_sorted, col_sorted_idx = torch.sort(col)
     indices = row[col_sorted_idx]
-    return create_from_csc(indptr, indices, val, shape=shape)
+    return from_csc(indptr, indices, val, shape=shape)
 
 
 def sparse_matrix_to_dense(A: SparseMatrix):