/tests/python/common/test_heterograph-apply-edges.py

import itertools
import unittest
from collections import Counter
from itertools import product

import backend as F

import dgl
import dgl.function as fn
import networkx as nx
import numpy as np
import pytest
import pytests_utils
import scipy.sparse as ssp
from dgl import DGLError
from pytests_utils import get_cases, parametrize_idtype
from scipy.sparse import rand

rfuncs = {"sum": fn.sum, "max": fn.max, "min": fn.min, "mean": fn.mean}
fill_value = {"sum": 0, "max": float("-inf")}
feat_size = 2


@unittest.skipIf(
    dgl.backend.backend_name != "pytorch", reason="Only support PyTorch for now"
)
def create_test_heterograph(idtype):
    # test heterograph from the docstring, plus a user -- wishes -- game relation
    # 3 users, 2 games, 2 developers
    # metagraph:
    #    ('user', 'follows', 'user'),
    #    ('user', 'plays', 'game'),
    #    ('user', 'wishes', 'game'),
    #    ('developer', 'develops', 'game')])

    g = dgl.heterograph(
        {
            ("user", "follows", "user"): ([0, 1, 2, 1], [0, 0, 1, 1]),
            ("user", "plays", "game"): ([0, 1, 2, 1], [0, 0, 1, 1]),
            ("user", "wishes", "game"): ([0, 1, 1], [0, 0, 1]),
            ("developer", "develops", "game"): ([0, 1, 0], [0, 1, 1]),
        },
        idtype=idtype,
        device=F.ctx(),
    )
    assert g.idtype == idtype
    assert g.device == F.ctx()
    return g


@parametrize_idtype
def test_unary_copy_u(idtype):
    def _test(mfunc):
        g = create_test_heterograph(idtype)

        x1 = F.randn((g.num_nodes("user"), feat_size))
        x2 = F.randn((g.num_nodes("developer"), feat_size))

        F.attach_grad(x1)
        F.attach_grad(x2)
        g.nodes["user"].data["h"] = x1
        g.nodes["developer"].data["h"] = x2

        #################################################################
        #  apply_edges() is called on each relation type separately
        #################################################################

        with F.record_grad():
            [
                g.apply_edges(fn.copy_u("h", "m"), etype=rel)
                for rel in g.canonical_etypes
            ]
            r1 = g["plays"].edata["m"]
            F.backward(r1, F.ones(r1.shape))
            n_grad1 = F.grad(g.ndata["h"]["user"])
        # TODO (Israt): clear not working
        g.edata["m"].clear()

        #################################################################
        #  apply_edges() is called on all relation types
        #################################################################

        g.apply_edges(fn.copy_u("h", "m"))
        r2 = g["plays"].edata["m"]
        F.backward(r2, F.ones(r2.shape))
        n_grad2 = F.grad(g.nodes["user"].data["h"])

        # correctness check
        def _print_error(a, b):
            for i, (x, y) in enumerate(
                zip(F.asnumpy(a).flatten(), F.asnumpy(b).flatten())
            ):
                if not np.allclose(x, y):
                    print("@{} {} v.s. {}".format(i, x, y))

        if not F.allclose(r1, r2):
            _print_error(r1, r2)
        assert F.allclose(r1, r2)
        if not F.allclose(n_grad1, n_grad2):
            print("node grad")
            _print_error(n_grad1, n_grad2)
        assert F.allclose(n_grad1, n_grad2)

    _test(fn.copy_u)


@parametrize_idtype
def test_unary_copy_e(idtype):
    def _test(mfunc):
        g = create_test_heterograph(idtype)
        feat_size = 2

        x1 = F.randn((4, feat_size))
        x2 = F.randn((4, feat_size))
        x3 = F.randn((3, feat_size))
        x4 = F.randn((3, feat_size))
        F.attach_grad(x1)
        F.attach_grad(x2)
        F.attach_grad(x3)
        F.attach_grad(x4)
        g["plays"].edata["eid"] = x1
        g["follows"].edata["eid"] = x2
        g["develops"].edata["eid"] = x3
        g["wishes"].edata["eid"] = x4

        #################################################################
        #  apply_edges() is called on each relation type separately
        #################################################################
        with F.record_grad():
            [
                g.apply_edges(fn.copy_e("eid", "m"), etype=rel)
                for rel in g.canonical_etypes
            ]
            r1 = g["develops"].edata["m"]
            F.backward(r1, F.ones(r1.shape))
            e_grad1 = F.grad(g["develops"].edata["eid"])

        #################################################################
        #  apply_edges() is called on all relation types
        #################################################################

        g.apply_edges(fn.copy_e("eid", "m"))
        r2 = g["develops"].edata["m"]
        F.backward(r2, F.ones(r2.shape))
        e_grad2 = F.grad(g["develops"].edata["eid"])

        # # correctness check
        def _print_error(a, b):
            for i, (x, y) in enumerate(
                zip(F.asnumpy(a).flatten(), F.asnumpy(b).flatten())
            ):
                if not np.allclose(x, y):
                    print("@{} {} v.s. {}".format(i, x, y))

        if not F.allclose(r1, r2):
            _print_error(r1, r2)
        assert F.allclose(r1, r2)
        if not F.allclose(e_grad1, e_grad2):
            print("edge grad")
            _print_error(e_grad1, e_grad2)
        assert F.allclose(e_grad1, e_grad2)

    _test(fn.copy_e)


@parametrize_idtype
def test_binary_op(idtype):
    def _test(lhs, rhs, binary_op):
        g = create_test_heterograph(idtype)

        n1 = F.randn((g.num_nodes("user"), feat_size))
        n2 = F.randn((g.num_nodes("developer"), feat_size))
        n3 = F.randn((g.num_nodes("game"), feat_size))

        x1 = F.randn((g.num_edges("plays"), feat_size))
        x2 = F.randn((g.num_edges("follows"), feat_size))
        x3 = F.randn((g.num_edges("develops"), feat_size))
        x4 = F.randn((g.num_edges("wishes"), feat_size))

        builtin_msg_name = "{}_{}_{}".format(lhs, binary_op, rhs)
        builtin_msg = getattr(fn, builtin_msg_name)

        #################################################################
        #  apply_edges() is called on each relation type separately
        #################################################################

        F.attach_grad(n1)
        F.attach_grad(n2)
        F.attach_grad(n3)
        g.nodes["user"].data["h"] = n1
        g.nodes["developer"].data["h"] = n2
        g.nodes["game"].data["h"] = n3
        F.attach_grad(x1)
        F.attach_grad(x2)
        F.attach_grad(x3)
        F.attach_grad(x4)
        g["plays"].edata["h"] = x1
        g["follows"].edata["h"] = x2
        g["develops"].edata["h"] = x3
        g["wishes"].edata["h"] = x4

        with F.record_grad():
            [
                g.apply_edges(builtin_msg("h", "h", "m"), etype=rel)
                for rel in g.canonical_etypes
            ]
            r1 = g["plays"].edata["m"]
            loss = F.sum(r1.view(-1), 0)
            F.backward(loss)
            n_grad1 = F.grad(g.nodes["game"].data["h"])

        #################################################################
        #  apply_edges() is called on all relation types
        #################################################################

        F.attach_grad(n1)
        F.attach_grad(n2)
        F.attach_grad(n3)
        g.nodes["user"].data["h"] = n1
        g.nodes["developer"].data["h"] = n2
        g.nodes["game"].data["h"] = n3
        F.attach_grad(x1)
        F.attach_grad(x2)
        F.attach_grad(x3)
        F.attach_grad(x4)
        g["plays"].edata["h"] = x1
        g["follows"].edata["h"] = x2
        g["develops"].edata["h"] = x3
        g["wishes"].edata["h"] = x4

        with F.record_grad():
            g.apply_edges(builtin_msg("h", "h", "m"))
            r2 = g["plays"].edata["m"]
            loss = F.sum(r2.view(-1), 0)
            F.backward(loss)
            n_grad2 = F.grad(g.nodes["game"].data["h"])

        # correctness check
        def _print_error(a, b):
            for i, (x, y) in enumerate(
                zip(F.asnumpy(a).flatten(), F.asnumpy(b).flatten())
            ):
                if not np.allclose(x, y):
                    print("@{} {} v.s. {}".format(i, x, y))

        if not F.allclose(r1, r2):
            _print_error(r1, r2)
        assert F.allclose(r1, r2)
        if n_grad1 is not None or n_grad2 is not None:
            if not F.allclose(n_grad1, n_grad2):
                print("node grad")
                _print_error(n_grad1, n_grad2)
            assert F.allclose(n_grad1, n_grad2)

    target = ["u", "v", "e"]
    for lhs, rhs in product(target, target):
        if lhs == rhs:
            continue
        for binary_op in ["add", "sub", "mul", "div", "dot"]:
            print(lhs, rhs, binary_op)
            _test(lhs, rhs, binary_op)


if __name__ == "__main__":
    test_unary_copy_u()
    test_unary_copy_e()