[Sparse] Namespace migration and doc update (#5088)

* update

* lint

* lint

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

docs/source/api/python/dgl.sparse_v0.rst

 .. _apibackend:
 
-dgl.mock_sparse
+dgl.sparse
 =================================
 
-`dgl_sparse` is a library for sparse operators that are commonly used in GNN models.
+`dgl.sparse` is a library for sparse operators that are commonly used in GNN models.
 
 .. warning::
     This is an experimental package. The sparse operators provided in this library do not guarantee the same performance as their message-passing api counterparts.
 
 Sparse matrix class
 -------------------------
-.. currentmodule:: dgl.mock_sparse
+.. currentmodule:: dgl.sparse
 
 .. class:: SparseMatrix
 
-    Class for creating a sparse matrix representation. The row and column indices of the sparse matrix can be the source
-    (row) and destination (column) indices of a homogeneous or heterogeneous graph.
+    Class for creating a sparse matrix representation
 
     There are a few ways to create a sparse matrix:
 
@@ -23,24 +22,24 @@ Sparse matrix class
     * In CSR format using row pointers and col indices, use :func:`create_from_csr`.
     * In CSC format using col pointers and row indices, use :func:`create_from_csc`.
 
-    For example, we can create COO matrix as follows:
+    For example, one can create COO matrices as follows:
 
-    Case1: Sparse matrix with row and column indices without values.
+    Case1: Sparse matrix with row and column indices without values
 
-        >>> src = torch.tensor([1, 1, 2])
-        >>> dst = torch.tensor([2, 4, 3])
-        >>> A = create_from_coo(src, dst)
+        >>> row = torch.tensor([1, 1, 2])
+        >>> col = torch.tensor([2, 4, 3])
+        >>> A = create_from_coo(row, col)
         >>> A
         SparseMatrix(indices=tensor([[1, 1, 2],
                                      [2, 4, 3]]),
                      values=tensor([1., 1., 1.]),
                      shape=(3, 5), nnz=3)
 
-    Case2: Sparse matrix with scalar/vector values. Following example is with
-    vector data.
+    Case2: Sparse matrix with scalar/vector values
 
+        >>> # vector values
         >>> val = torch.tensor([[1, 1], [2, 2], [3, 3]])
-        >>> A = create_from_coo(src, dst, val)
+        >>> A = create_from_coo(row, col, val)
         SparseMatrix(indices=tensor([[1, 1, 2],
                                      [2, 4, 3]]),
                      values=tensor([[1, 1],
@@ -48,7 +47,7 @@ Sparse matrix class
                                     [3, 3]]),
                      shape=(3, 5), nnz=3)
 
-    Similarly, we can create CSR matrix as follows:
+    Similarly, one can create a CSR matrix as follows:
 
         >>> indptr = torch.tensor([0, 1, 2, 5])
         >>> indices = torch.tensor([1, 2, 0, 1, 2])
@@ -64,23 +63,37 @@ Sparse matrix class
                                     [5, 5]]),
                      shape=(3, 3), nnz=5)
 
-Sparse matrix class attributes
-------------------------------
+Creators
+````````
 
 .. autosummary::
+    :toctree: ../../generated/
+
+    create_from_coo
+    create_from_csr
+    create_from_csc
+    val_like
+
+Attributes and methods
+``````````````````````
+
+.. autosummary::
+    :toctree: ../../generated/
 
     SparseMatrix.shape
     SparseMatrix.nnz
     SparseMatrix.dtype
     SparseMatrix.device
+    SparseMatrix.val
+    SparseMatrix.__repr__
     SparseMatrix.row
     SparseMatrix.col
-    SparseMatrix.val
-    __call__
     SparseMatrix.indices
     SparseMatrix.coo
     SparseMatrix.csr
     SparseMatrix.csc
+    SparseMatrix.coalesce
+    SparseMatrix.has_duplicate
     SparseMatrix.dense
     SparseMatrix.t
     SparseMatrix.T
@@ -90,64 +103,67 @@ Sparse matrix class attributes
     SparseMatrix.smax
     SparseMatrix.smin
     SparseMatrix.smean
-    SparseMatrix.__neg__
-    SparseMatrix.inv
+    SparseMatrix.neg
     SparseMatrix.softmax
     SparseMatrix.__matmul__
 
-.. autosummary::
-    :toctree: ../../generated/
-
-    create_from_coo
-    create_from_csr
-    create_from_csc
-
 Diagonal matrix class
 -------------------------
-.. currentmodule:: dgl.mock_sparse
+.. currentmodule:: dgl.sparse
 
-.. autoclass:: DiagMatrix
+.. class:: DiagMatrix
 
-Diagonal matrix class attributes
---------------------------------
+Creators
+````````
 
 .. autosummary::
+    :toctree: ../../generated/
+
+    diag
+    identity
+
+Attributes and methods
+``````````````````````
+
+.. autosummary::
+    :toctree: ../../generated/
 
-    DiagMatrix.val
     DiagMatrix.shape
-    DiagMatrix.__call__
     DiagMatrix.nnz
     DiagMatrix.dtype
     DiagMatrix.device
+    DiagMatrix.val
+    DiagMatrix.__repr__
     DiagMatrix.as_sparse
+    DiagMatrix.dense
     DiagMatrix.t
     DiagMatrix.T
     DiagMatrix.transpose
-    DiagMatrix.reduce
-    DiagMatrix.sum
-    DiagMatrix.smax
-    DiagMatrix.smin
-    DiagMatrix.smean
-    DiagMatrix.__neg__
+    DiagMatrix.neg
     DiagMatrix.inv
-    DiagMatrix.softmax
     DiagMatrix.__matmul__
 
-.. autosummary::
-    :toctree: ../../generated/
-
-    diag
-    identity
-
 Operators
 ---------
-.. currentmodule:: dgl.mock_sparse
+.. currentmodule:: dgl.sparse
 
 .. autosummary::
     :toctree: ../../generated/
 
+    sp_add
+    sp_mul
+    sp_power
+    diag_add
+    diag_sub
+    diag_mul
+    diag_div
+    diag_power
+    add
+    power
     spmm
-    spspmm
     bspmm
-    bspspmm
+    spspmm
+    mm
     sddmm
+    bsddmm
+    softmax

examples/sparse/appnp.py

@@ -3,7 +3,7 @@
 (https://arxiv.org/abs/1810.05997)
 """
 
-import dgl.mock_sparse2 as dglsp
+import dgl.sparse as dglsp
 import torch
 import torch.nn as nn
 import torch.nn.functional as F

examples/sparse/c_and_s.py

@@ -2,7 +2,7 @@
 [Combining Label Propagation and Simple Models Out-performs
 Graph Neural Networks](https://arxiv.org/abs/2010.13993)
 """
-import dgl.mock_sparse2 as dglsp
+import dgl.sparse as dglsp
 import torch
 import torch.nn as nn
 import torch.nn.functional as F

examples/sparse/gat.py

@@ -3,7 +3,7 @@
 (https://arxiv.org/abs/1710.10903)
 """
 
-import dgl.mock_sparse2 as dglsp
+import dgl.sparse as dglsp
 import torch
 import torch.nn as nn
 import torch.nn.functional as F

examples/sparse/gcn.py

@@ -3,7 +3,7 @@
 (https://arxiv.org/abs/1609.02907)
 """
 
-import dgl.mock_sparse2 as dglsp
+import dgl.sparse as dglsp
 import torch
 import torch.nn as nn
 import torch.nn.functional as F

examples/sparse/gcnii.py

@@ -5,7 +5,7 @@
 
 import math
 
-import dgl.mock_sparse2 as dglsp
+import dgl.sparse as dglsp
 
 import torch
 import torch.nn as nn

examples/sparse/hgnn.py

 """
 Hypergraph Neural Networks (https://arxiv.org/pdf/1809.09401.pdf)
 """
-import dgl.mock_sparse2 as dglsp
+import dgl.sparse as dglsp
 import torch
 import torch.nn as nn
 import torch.nn.functional as F

examples/sparse/hypergraphatt.py

@@ -4,7 +4,7 @@ Hypergraph Convolution and Hypergraph Attention
 """
 import argparse
 
-import dgl.mock_sparse2 as dglsp
+import dgl.sparse as dglsp
 
 import torch
 import torch.nn as nn

examples/sparse/sgc.py

@@ -3,7 +3,7 @@
 (https://arxiv.org/abs/1902.07153)
 """
 
-import dgl.mock_sparse2 as dglsp
+import dgl.sparse as dglsp
 import torch
 import torch.nn as nn
 import torch.nn.functional as F

examples/sparse/sign.py

@@ -6,7 +6,7 @@ This example shows a simplified version of SIGN: a precomputed 2-hops diffusion
 operator on top of symmetrically normalized adjacency matrix A_hat.
 """
 
-import dgl.mock_sparse2 as dglsp
+import dgl.sparse as dglsp
 import torch
 import torch.nn as nn
 import torch.nn.functional as F

examples/sparse/twirls.py

@@ -10,7 +10,7 @@ with attention.
 
 import argparse
 
-import dgl.mock_sparse2 as dglsp
+import dgl.sparse as dglsp
 
 import torch
 import torch.nn as nn

python/dgl/mock_sparse2/elementwise_op_diag.py → python/dgl/sparse/elementwise_op_diag.py

 """DGL elementwise operators for diagonal matrix module."""
 from typing import Union
 
-from .diag_matrix import DiagMatrix, diag
+from .diag_matrix import diag, DiagMatrix
 from .sparse_matrix import SparseMatrix
 
 __all__ = ["diag_add", "diag_sub", "diag_mul", "diag_div", "diag_power"]

python/dgl/mock_sparse2/softmax.py → python/dgl/sparse/softmax.py

@@ -57,4 +57,5 @@ def softmax(A: SparseMatrix) -> SparseMatrix:
     """
     return SparseMatrix(torch.ops.dgl_sparse.softmax(A.c_sparse_matrix))
 
+
 SparseMatrix.softmax = softmax