dgl/tests/python/pytorch/sparse/test_elementwise_op.py

import operator

import backend as F

import dgl.sparse as dglsp
import pytest
import torch

from dgl.sparse import diag, power


@pytest.mark.parametrize("opname", ["add", "sub", "mul", "truediv"])
def test_diag_op_diag(opname):
    op = getattr(operator, opname)
    ctx = F.ctx()
    shape = (3, 4)
    D1 = diag(torch.arange(1, 4).to(ctx), shape=shape)
    D2 = diag(torch.arange(10, 13).to(ctx), shape=shape)
    result = op(D1, D2)
    assert torch.allclose(result.val, op(D1.val, D2.val), rtol=1e-4, atol=1e-4)
    assert result.shape == D1.shape


@pytest.mark.parametrize(
    "v_scalar", [2, 2.5, torch.tensor(2), torch.tensor(2.5)]
)
def test_diag_op_scalar(v_scalar):
    ctx = F.ctx()
    shape = (3, 4)
    D1 = diag(torch.arange(1, 4).to(ctx), shape=shape)

    # D * v
    D2 = D1 * v_scalar
    assert torch.allclose(D1.val * v_scalar, D2.val, rtol=1e-4, atol=1e-4)
    assert D1.shape == D2.shape

    # v * D
    D2 = v_scalar * D1
    assert torch.allclose(v_scalar * D1.val, D2.val, rtol=1e-4, atol=1e-4)
    assert D1.shape == D2.shape

    # D / v
    D2 = D1 / v_scalar
    assert torch.allclose(D1.val / v_scalar, D2.val, rtol=1e-4, atol=1e-4)
    assert D1.shape == D2.shape

    # D ^ v
    D1 = diag(torch.arange(1, 4).to(ctx))
    D2 = D1**v_scalar
    assert torch.allclose(D1.val**v_scalar, D2.val, rtol=1e-4, atol=1e-4)
    assert D1.shape == D2.shape

    # pow(D, v)
    D2 = power(D1, v_scalar)
    assert torch.allclose(D1.val**v_scalar, D2.val, rtol=1e-4, atol=1e-4)
    assert D1.shape == D2.shape

    with pytest.raises(TypeError):
        D1 + v_scalar
    with pytest.raises(TypeError):
        v_scalar + D1

    with pytest.raises(TypeError):
        D1 - v_scalar
    with pytest.raises(TypeError):
        v_scalar - D1


@pytest.mark.parametrize("val_shape", [(), (2,)])
@pytest.mark.parametrize("opname", ["add", "sub"])
def test_addsub_coo(val_shape, opname):
    op = getattr(operator, opname)
    func = getattr(dglsp, opname)
    ctx = F.ctx()
    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 = dglsp.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 = dglsp.from_coo(row, col, val, shape=A.shape)

    C1 = op(A, B).to_dense()
    C2 = func(A, B).to_dense()
    dense_C = op(A.to_dense(), B.to_dense())

    assert torch.allclose(dense_C, C1)
    assert torch.allclose(dense_C, C2)

    with pytest.raises(TypeError):
        op(A, 2)
    with pytest.raises(TypeError):
        op(2, A)


@pytest.mark.parametrize("val_shape", [(), (2,)])
@pytest.mark.parametrize("opname", ["add", "sub"])
def test_addsub_csr(val_shape, opname):
    op = getattr(operator, opname)
    func = getattr(dglsp, opname)
    ctx = F.ctx()
    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 = dglsp.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 = dglsp.from_csr(indptr, indices, val, shape=A.shape)

    C1 = op(A, B).to_dense()
    C2 = func(A, B).to_dense()
    dense_C = op(A.to_dense(), B.to_dense())

    assert torch.allclose(dense_C, C1)
    assert torch.allclose(dense_C, C2)

    with pytest.raises(TypeError):
        op(A, 2)
    with pytest.raises(TypeError):
        op(2, A)


@pytest.mark.parametrize("val_shape", [(), (2,)])
@pytest.mark.parametrize("opname", ["add", "sub"])
def test_addsub_csc(val_shape, opname):
    op = getattr(operator, opname)
    func = getattr(dglsp, opname)
    ctx = F.ctx()
    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 = dglsp.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 = dglsp.from_csc(indptr, indices, val, shape=A.shape)

    C1 = op(A, B).to_dense()
    C2 = func(A, B).to_dense()
    dense_C = op(A.to_dense(), B.to_dense())

    assert torch.allclose(dense_C, C1)
    assert torch.allclose(dense_C, C2)

    with pytest.raises(TypeError):
        op(A, 2)
    with pytest.raises(TypeError):
        op(2, A)


@pytest.mark.parametrize("val_shape", [(), (2,)])
@pytest.mark.parametrize("opname", ["add", "sub"])
def test_addsub_diag(val_shape, opname):
    op = getattr(operator, opname)
    func = getattr(dglsp, opname)
    ctx = F.ctx()
    shape = (3, 4)
    val_shape = (shape[0],) + val_shape
    D1 = dglsp.diag(torch.randn(val_shape).to(ctx), shape=shape)
    D2 = dglsp.diag(torch.randn(val_shape).to(ctx), shape=shape)

    C1 = op(D1, D2).to_dense()
    C2 = func(D1, D2).to_dense()
    dense_C = op(D1.to_dense(), D2.to_dense())

    assert torch.allclose(dense_C, C1)
    assert torch.allclose(dense_C, C2)

    with pytest.raises(TypeError):
        op(D1, 2)
    with pytest.raises(TypeError):
        op(2, D1)


@pytest.mark.parametrize("val_shape", [(), (2,)])
def test_add_sparse_diag(val_shape):
    ctx = F.ctx()
    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 = dglsp.from_coo(row, col, val)

    shape = (3, 4)
    val_shape = (shape[0],) + val_shape
    D = dglsp.diag(torch.randn(val_shape).to(ctx), shape=shape)

    sum1 = (A + D).to_dense()
    sum2 = (D + A).to_dense()
    sum3 = dglsp.add(A, D).to_dense()
    sum4 = dglsp.add(D, A).to_dense()
    dense_sum = A.to_dense() + D.to_dense()

    assert torch.allclose(dense_sum, sum1)
    assert torch.allclose(dense_sum, sum2)
    assert torch.allclose(dense_sum, sum3)
    assert torch.allclose(dense_sum, sum4)


@pytest.mark.parametrize("val_shape", [(), (2,)])
def test_sub_sparse_diag(val_shape):
    ctx = F.ctx()
    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 = dglsp.from_coo(row, col, val)

    shape = (3, 4)
    val_shape = (shape[0],) + val_shape
    D = dglsp.diag(torch.randn(val_shape).to(ctx), shape=shape)

    diff1 = (A - D).to_dense()
    diff2 = (D - A).to_dense()
    diff3 = dglsp.sub(A, D).to_dense()
    diff4 = dglsp.sub(D, A).to_dense()
    dense_diff = A.to_dense() - D.to_dense()

    assert torch.allclose(dense_diff, diff1)
    assert torch.allclose(dense_diff, -diff2)
    assert torch.allclose(dense_diff, diff3)
    assert torch.allclose(dense_diff, -diff4)


@pytest.mark.parametrize("op", ["pow"])
def test_error_op_sparse_diag(op):
    ctx = F.ctx()
    row = torch.tensor([1, 0, 2]).to(ctx)
    col = torch.tensor([0, 3, 2]).to(ctx)
    val = torch.randn(row.shape).to(ctx)
    A = dglsp.from_coo(row, col, val)

    shape = (3, 4)
    D = dglsp.diag(torch.randn(row.shape[0]).to(ctx), shape=shape)

    with pytest.raises(TypeError):
        getattr(operator, op)(A, D)
    with pytest.raises(TypeError):
        getattr(operator, op)(D, A)